{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/adamenders/Dropbox/Value Polarization and Affective Polarization/Code and Data/For Dataverse/Stata log file.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}20 Mar 2020, 16:39:25

{com}. use "/Users/adamenders/Dropbox/Value Polarization and Affective Polarization/Code and Data/For Dataverse/anes_timeseries_2016_Stata12.dta"

. do "/var/folders/xb/ddtsf7g93xd57f7hhtnm9lyc0000gp/T//SD07120.000000"
{txt}
{com}. ****
. ** Clean/recode variables
. ****
. 
. * Case ID
. gen caseid = V160001
{txt}
{com}. 
. 
. * Year 
. gen year = 2016
{txt}
{com}. 
. gen yearalt1 = 7
{txt}
{com}. 
. 
. * Weight
. gen weight = V160101
{txt}
{com}. 
. 
. * Internet
. gen internet = 0
{txt}
{com}. replace internet = 1 if V160501 == 2
{txt}(3,090 real changes made)

{com}. 
. 
. * Feeling thermometers
. gen trumptherm = V161087
{txt}
{com}. replace trumptherm = . if trumptherm < 0
{txt}(41 real changes made, 41 to missing)

{com}. replace trumptherm = . if trumptherm > 100
{txt}(0 real changes made)

{com}. recode trumptherm (100=97) (99=97) (98=97)
{txt}(trumptherm: 255 changes made)

{com}. 
. gen clintontherm = V161086
{txt}
{com}. replace clintontherm = . if clintontherm < 0
{txt}(38 real changes made, 38 to missing)

{com}. replace clintontherm = . if clintontherm > 100
{txt}(0 real changes made)

{com}. recode clintontherm (100=97) (99=97) (98=97)
{txt}(clintontherm: 256 changes made)

{com}. 
. gen liberaltherm = V162097
{txt}
{com}. replace liberaltherm = . if liberaltherm < 0
{txt}(663 real changes made, 663 to missing)

{com}. replace liberaltherm = . if liberaltherm > 100
{txt}(20 real changes made, 20 to missing)

{com}. recode liberaltherm (100=97) (99=97) (98=97)
{txt}(liberaltherm: 180 changes made)

{com}. 
. gen conservtherm = V162101
{txt}
{com}. replace conservtherm = . if conservtherm < 0
{txt}(657 real changes made, 657 to missing)

{com}. replace conservtherm = . if conservtherm > 100
{txt}(19 real changes made, 19 to missing)

{com}. recode conservtherm (100=97) (99=97) (98=97)
{txt}(conservtherm: 256 changes made)

{com}. 
. gen dempartytherm = V161095
{txt}
{com}. replace dempartytherm = . if dempartytherm < 0
{txt}(70 real changes made, 70 to missing)

{com}. replace dempartytherm = . if dempartytherm > 100
{txt}(0 real changes made)

{com}. recode dempartytherm (100=97) (99=97) (98=97)
{txt}(dempartytherm: 262 changes made)

{com}. 
. gen reppartytherm = V161096
{txt}
{com}. replace reppartytherm = . if reppartytherm < 0
{txt}(86 real changes made, 86 to missing)

{com}. replace reppartytherm = . if reppartytherm > 100
{txt}(0 real changes made)

{com}. recode reppartytherm (100=97) (99=97) (98=97)
{txt}(reppartytherm: 126 changes made)

{com}. 
. 
. gen partythermdiff = abs(dempartytherm - reppartytherm)
{txt}(95 missing values generated)

{com}. 
. gen diffcandtherm = abs(clintontherm - trumptherm)
{txt}(62 missing values generated)

{com}. 
. gen ideothermdiff = abs(liberaltherm - conservtherm)
{txt}(705 missing values generated)

{com}. 
. 
. * Party identification
. gen pid = V161158x
{txt}
{com}. replace pid = . if pid < 1
{txt}(23 real changes made, 23 to missing)

{com}. replace pid = pid - 4
{txt}(4,248 real changes made)

{com}. 
. gen rep = 1 if pid > 0
{txt}(2,519 missing values generated)

{com}. replace rep = 0 if pid < 0
{txt}(1,940 real changes made)

{com}. 
. gen pidstrength = abs(pid)
{txt}(23 missing values generated)

{com}. 
. 
. * Ideology
. gen ideo = V162171
{txt}
{com}. replace ideo = . if ideo < 1
{txt}(631 real changes made, 631 to missing)

{com}. replace ideo = . if ideo > 7
{txt}(590 real changes made, 590 to missing)

{com}. replace ideo = ideo - 4
{txt}(3,050 real changes made)

{com}. 
. gen conserv = 1 if ideo > 0
{txt}(1,838 missing values generated)

{com}. replace conserv = 0 if ideo < 0
{txt}(997 real changes made)

{com}. 
. gen ideostrength = abs(ideo)
{txt}(1,221 missing values generated)

{com}. 
. 
. * Partisan sorting (ala Mason 2015)
. replace ideostrength = ideostrength + 1
{txt}(3,050 real changes made)

{com}. replace pidstrength = pidstrength + 1
{txt}(4,248 real changes made)

{com}. gen sorting = abs(pid - (-1 * ideo)) * ideostrength * pidstrength
{txt}(1,228 missing values generated)

{com}. 
. 
. * Church attendance
. gen church = V161245
{txt}
{com}. replace church = . if church > 5
{txt}(0 real changes made)

{com}. replace church = . if church < 1
{txt}(1,722 real changes made, 1,722 to missing)

{com}. recode church (5=1) (4=2) (3=3) (2=4) (1=5)
{txt}(church: 2078 changes made)

{com}. 
. 
. * Interest in following campaign
. gen interest = V161004
{txt}
{com}. replace interest = . if V161004 < 1
{txt}(0 real changes made)

{com}. recode interest (1=2) (2=1) (3=0)
{txt}(interest: 4271 changes made)

{com}. 
. 
. * Retrospective economic evaluations
. gen econeval = V161140
{txt}
{com}. replace econeval = . if V161140 < 1
{txt}(12 real changes made, 12 to missing)

{com}. recode econeval (3=0) (2=1) (1=2)
{txt}(econeval: 4259 changes made)

{com}. 
. 
. * Race
. gen white = 1 if V161310x == 1
{txt}(1,233 missing values generated)

{com}. replace white = 0 if V161310x != 1
{txt}(1,233 real changes made)

{com}. 
. gen black = 1 if V161310x == 2
{txt}(3,873 missing values generated)

{com}. replace black = 0 if V161310x != 2
{txt}(3,873 real changes made)

{com}. 
. gen hispanic = 1 if V161310x == 5
{txt}(3,821 missing values generated)

{com}. replace hispanic = 0 if V161310x != 5
{txt}(3,821 real changes made)

{com}. 
. 
. * Do whatever is necessary for equal opportunity
. * Note: This variable is reverse coded so that higher values indicate
. * more conservative attitudes
. gen equalopp = V162243 - 1
{txt}
{com}. replace equalopp = . if equalopp < 0
{txt}(639 real changes made, 639 to missing)

{com}. 
. 
. *Big problem is not giving everyone an equal chance
. gen equalchance = V162245
{txt}
{com}. replace equalchance = . if equalchance < 1
{txt}(644 real changes made, 644 to missing)

{com}. recode equalchance (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(equalchance: 3627 changes made)

{com}. 
. 
. * Better off if we worried less about equality
. * Note: This variable is reverse coded so that higher values indicate
. * more conservative attitudes
. gen lessequal = V162244
{txt}
{com}. replace lessequal = . if lessequal < 1
{txt}(641 real changes made, 641 to missing)

{com}. recode lessequal (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(lessequal: 3630 changes made)

{com}. 
. 
. * Many fewer problems if people were treated equally
. gen fewer = V162246 - 1
{txt}
{com}. replace fewer = . if fewer < 0
{txt}(642 real changes made, 642 to missing)

{com}. 
. 
. *Adjusting views of moral behavior
. gen changing = V162207 - 1
{txt}
{com}. replace changing = . if changing < 0
{txt}(631 real changes made, 631 to missing)

{com}.  
.  
. * Newer lifestyles contributing to a breakdown in society
. * Note: This variable is reverse coded so that higher values indicate*
. * more conservative attitudes
. gen lifestyles = V162208
{txt}
{com}. replace lifestyles = . if lifestyles < 1
{txt}(637 real changes made, 637 to missing)

{com}. recode lifestyles (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(lifestyles: 3634 changes made)

{com}. 
. 
. * Tolerant of people who choose to live according to their own moral standards
. gen standards = V162209 - 1
{txt}
{com}. replace standards = . if standards < 0
{txt}(639 real changes made, 639 to missing)

{com}.  
.  
. * More emphasis on traditional family ties*
. * Note: This variable is reverse coded so that higher values indicate
. * more conservative attitudes
. gen family = V162210
{txt}
{com}. replace family = . if family < 1
{txt}(636 real changes made, 636 to missing)

{com}. recode family (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(family: 3635 changes made)

{com}. 
. 
. * Creating values scales
. alpha equalopp equalchance lessequal fewer changing lifestyles standards ///
>         family, detail item generate(values) casewise

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
equalopp{col 14}{c |}{res}{col 16}3600{col 24}+{col 31} 0.4820{col 45} 0.3340{col 59} .4940821{col 73} 0.7563
{txt}equalchance{col 14}{c |}{res}{col 16}3600{col 24}+{col 31} 0.5929{col 45} 0.4434{col 59} .4512866{col 73} 0.7395
{txt}lessequal{col 14}{c |}{res}{col 16}3600{col 24}+{col 31} 0.6961{col 45} 0.5477{col 59} .4005481{col 73} 0.7195
{txt}fewer{col 14}{c |}{res}{col 16}3600{col 24}+{col 31} 0.5751{col 45} 0.4321{col 59} .4613861{col 73} 0.7416
{txt}changing{col 14}{c |}{res}{col 16}3600{col 24}+{col 31} 0.5800{col 45} 0.3972{col 59}   .44657{col 73} 0.7496
{txt}lifestyles{col 14}{c |}{res}{col 16}3600{col 24}+{col 31} 0.6857{col 45} 0.5391{col 59}  .407362{col 73} 0.7214
{txt}standards{col 14}{c |}{res}{col 16}3600{col 24}+{col 31} 0.6195{col 45} 0.4757{col 59} .4420177{col 73} 0.7340
{txt}family{col 14}{c |}{res}{col 16}3600{col 24}+{col 31} 0.6522{col 45} 0.5051{col 59} .4247816{col 73} 0.7283
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .4410043{col 73} 0.7619
{txt}{hline 13}{c BT}{hline 65}

Interitem covariances (obs=3600 in all pairs)

                equalopp  equalchance    lessequal        fewer     changing
   equalopp  {res}     1.0892
{txt}equalchance  {res}     0.3164       1.4021
{txt}  lessequal  {res}     0.3785       0.8107       1.8934
{txt}      fewer  {res}     0.4960       0.4246       0.5236       1.2422
{txt}   changing  {res}     0.2903       0.1844       0.3904       0.3668       1.9151
{txt} lifestyles  {res}     0.1269       0.4393       0.7213       0.2900       0.5624
{txt}  standards  {res}     0.2687       0.2870       0.4279       0.3346       0.6730
{txt}     family  {res}     0.0958       0.4088       0.6842       0.2233       0.5029

             {txt} lifestyles    standards       family
 lifestyles  {res}     1.7895
{txt}  standards  {res}     0.6079       1.4073
{txt}     family  {res}     1.0458       0.4668       1.6115
{txt}
{com}. 
. alpha equalopp equalchance lessequal fewer, detail item ///
>         generate(equality) casewise

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
equalopp{col 14}{c |}{res}{col 16}3617{col 24}+{col 31} 0.6441{col 45} 0.4031{col 59} .5852436{col 73} 0.6542
{txt}equalchance{col 14}{c |}{res}{col 16}3617{col 24}+{col 31} 0.7344{col 45} 0.4938{col 59} .4661017{col 73} 0.5975
{txt}lessequal{col 14}{c |}{res}{col 16}3617{col 24}+{col 31} 0.7718{col 45} 0.4993{col 59} .4122016{col 73} 0.5976
{txt}fewer{col 14}{c |}{res}{col 16}3617{col 24}+{col 31} 0.7095{col 45} 0.4755{col 59} .5019636{col 73} 0.6109
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .4913776{col 73} 0.6822
{txt}{hline 13}{c BT}{hline 65}

Interitem covariances (obs=3617 in all pairs)

                equalopp  equalchance    lessequal        fewer
   equalopp  {res}     1.0871
{txt}equalchance  {res}     0.3170       1.4034
{txt}  lessequal  {res}     0.3793       0.8096       1.8927
{txt}      fewer  {res}     0.4962       0.4234       0.5227       1.2441
{txt}
{com}.         
. alpha changing lifestyles standards family, detail item ///
>         generate(morality) casewise

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
changing{col 14}{c |}{res}{col 16}3620{col 24}+{col 31} 0.6943{col 45} 0.4166{col 59} .7056471{col 73} 0.7021
{txt}lifestyles{col 14}{c |}{res}{col 16}3620{col 24}+{col 31} 0.7869{col 45} 0.5759{col 59} .5474184{col 73} 0.5991
{txt}standards{col 14}{c |}{res}{col 16}3620{col 24}+{col 31} 0.6997{col 45} 0.4764{col 59} .7018685{col 73} 0.6628
{txt}family{col 14}{c |}{res}{col 16}3620{col 24}+{col 31} 0.7515{col 45} 0.5347{col 59} .6131666{col 73} 0.6272
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .6420252{col 73} 0.7117
{txt}{hline 13}{c BT}{hline 65}

Interitem covariances (obs=3620 in all pairs)

              changing  lifestyles   standards      family
  changing  {res}    1.9181
{txt}lifestyles  {res}    0.5587      1.7910
{txt} standards  {res}    0.6754      0.6055      1.4103
{txt}    family  {res}    0.5012      1.0458      0.4657      1.6101
{txt}
{com}. 
. 
. * Party placement on ideological scale
. gen demideo = V161130
{txt}
{com}. replace demideo = . if demideo > 7
{txt}(0 real changes made)

{com}. replace demideo = . if demideo < 1
{txt}(94 real changes made, 94 to missing)

{com}. recode demideo (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demideo: 4177 changes made)

{com}. 
. gen repideo = V161131
{txt}
{com}. replace repideo = . if repideo > 7
{txt}(0 real changes made)

{com}. replace repideo = . if repideo < 1
{txt}(108 real changes made, 108 to missing)

{com}. recode repideo (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repideo: 4163 changes made)

{com}. 
. gen diffideoplace = abs(demideo - repideo)
{txt}(113 missing values generated)

{com}. 
. 
. * Candidate placement on ideological scale
. gen dcandideo = V161128
{txt}
{com}. replace dcandideo = . if dcandideo > 7
{txt}(0 real changes made)

{com}. replace dcandideo = . if dcandideo < 1
{txt}(114 real changes made, 114 to missing)

{com}. recode dcandideo (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(dcandideo: 4157 changes made)

{com}. 
. gen rcandideo = V161129
{txt}
{com}. replace rcandideo = . if rcandideo > 7
{txt}(0 real changes made)

{com}. replace rcandideo = . if rcandideo < 1
{txt}(167 real changes made, 167 to missing)

{com}. recode rcandideo (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(rcandideo: 4104 changes made)

{com}. 
. gen diffcandideo = abs(dcandideo - rcandideo)   
{txt}(191 missing values generated)

{com}. 
. 
. * Self and candidate placement on guaranteed jobs scale 
. gen selfjobs = V161189
{txt}
{com}. replace selfjobs = . if selfjobs < 1
{txt}(16 real changes made, 16 to missing)

{com}. replace selfjobs = . if selfjobs >= 8
{txt}(481 real changes made, 481 to missing)

{com}. recode selfjobs (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfjobs: 3774 changes made)

{com}. label define jobslab -3 "Government see to job and good standard of living" ///
>         3 "Government let each person get ahead on his own"
{txt}
{com}. label values selfjobs jobslab
{txt}
{com}. 
. gen demjobs = V161190
{txt}
{com}. replace demjobs = . if demjobs < 1
{txt}(92 real changes made, 92 to missing)

{com}. replace demjobs = . if demjobs >= 8
{txt}(0 real changes made)

{com}. recode demjobs (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demjobs: 4179 changes made)

{com}. label values demjobs jobslab
{txt}
{com}. 
. gen repjobs = V161191
{txt}
{com}. replace repjobs = . if repjobs < 1
{txt}(98 real changes made, 98 to missing)

{com}. replace repjobs = . if repjobs >= 8
{txt}(0 real changes made)

{com}. recode repjobs (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repjobs: 4173 changes made)

{com}. label values repjobs jobslab
{txt}
{com}. 
. gen pdiffjobs = abs(demjobs - repjobs)
{txt}(117 missing values generated)

{com}. 
. 
. * Self and candidate placement on aid to blacks scale 
. gen selfaid = V161198
{txt}
{com}. replace selfaid = . if selfaid < 1
{txt}(29 real changes made, 29 to missing)

{com}. replace selfaid = . if selfaid >= 8
{txt}(488 real changes made, 488 to missing)

{com}. recode selfaid (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfaid: 3754 changes made)

{com}. label define aidlab -3 "Government should help minority groups" ///
>         3 "Minority groups should help themselves"
{txt}
{com}. label values selfaid aidlab
{txt}
{com}. 
. gen demaid = V161199
{txt}
{com}. replace demaid = . if demaid < 1
{txt}(109 real changes made, 109 to missing)

{com}. replace demaid = . if demaid >= 8
{txt}(0 real changes made)

{com}. recode demaid (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demaid: 4162 changes made)

{com}. label values demaid aidlab
{txt}
{com}. 
. gen repaid = V161200
{txt}
{com}. replace repaid = . if repaid < 1
{txt}(104 real changes made, 104 to missing)

{com}. replace repaid = . if repaid >= 8
{txt}(0 real changes made)

{com}. recode repaid (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repaid: 4167 changes made)

{com}. label values repaid aidlab
{txt}
{com}. 
. gen pdiffaid = abs(demaid - repaid)
{txt}(128 missing values generated)

{com}. 
. 
. * Self and candidate placement on government services scale 
. gen selfservice = V161178
{txt}
{com}. replace selfservice = . if selfservice < 1
{txt}(17 real changes made, 17 to missing)

{com}. replace selfservice = . if selfservice >= 8
{txt}(626 real changes made, 626 to missing)

{com}. recode selfservice (1=3) (2=2) (3=1) (4=0) (5=-1) (6=-2) (7=-3)
{txt}(selfservice: 3183 changes made)

{com}. label define servicelab -3 "Government should provide many more services" ///
>         3 "Government should provide many ewer services"
{txt}
{com}. label values selfservice servicelab
{txt}
{com}. 
. gen demservice = V161179
{txt}
{com}. replace demservice = . if demservice < 1
{txt}(81 real changes made, 81 to missing)

{com}. replace demservice = . if demservice >= 8
{txt}(0 real changes made)

{com}. recode demservice (1=3) (2=2) (3=1) (4=0) (5=-1) (6=-2) (7=-3)
{txt}(demservice: 4081 changes made)

{com}. label values demservice servicelab
{txt}
{com}. 
. gen repservice = V161180
{txt}
{com}. replace repservice = . if repservice < 1
{txt}(109 real changes made, 109 to missing)

{com}. replace repservice = . if repservice >= 8
{txt}(0 real changes made)

{com}. recode repservice (1=3) (2=2) (3=1) (4=0) (5=-1) (6=-2) (7=-3)
{txt}(repservice: 3183 changes made)

{com}. label values repservice servicelab
{txt}
{com}. 
. gen pdiffservice = abs(demservice - repservice)
{txt}(126 missing values generated)

{com}. 
. 
. * Self and candidate placement on defense spending scale 
. gen selfdefense = V161181
{txt}
{com}. replace selfdefense = . if selfdefense < 1
{txt}(20 real changes made, 20 to missing)

{com}. replace selfdefense = . if selfdefense >= 8
{txt}(568 real changes made, 568 to missing)

{com}. recode selfdefense (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfdefense: 3683 changes made)

{com}. label define defenselab -3 "Greatly decrease defense spending" ///
>         3 "Greatly increase defense spending"
{txt}
{com}. label values selfdefense defenselab
{txt}
{com}. 
. gen demdefense = V161182
{txt}
{com}. replace demdefense = . if demdefense < 1
{txt}(100 real changes made, 100 to missing)

{com}. replace demdefense = . if demdefense >= 8
{txt}(0 real changes made)

{com}. recode demdefense (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demdefense: 4171 changes made)

{com}. label values demdefense defenselab
{txt}
{com}. 
. gen repdefense = V161183
{txt}
{com}. replace repdefense = . if repdefense < 1
{txt}(102 real changes made, 102 to missing)

{com}. replace repdefense = . if repdefense >= 8
{txt}(0 real changes made)

{com}. recode repdefense (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repdefense: 4169 changes made)

{com}. label values repdefense defenselab
{txt}
{com}. 
. gen pdiffdefense = abs(demdefense - repdefense)
{txt}(128 missing values generated)

{com}. 
. 
. * Self and candidate placement on government health insurance scale
. gen selfinsure = V161184
{txt}
{com}. replace selfinsure = . if selfinsure < 1
{txt}(15 real changes made, 15 to missing)

{com}. replace selfinsure = . if selfinsure >= 8
{txt}(490 real changes made, 490 to missing)

{com}. recode selfinsure (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfinsure: 3766 changes made)

{com}. label define insurelab -3 "Government insurance plan" 3 "Private insurance plan"
{txt}
{com}. label values selfinsure insurelab
{txt}
{com}. 
. gen deminsure = V161185
{txt}
{com}. replace deminsure = . if deminsure < 1
{txt}(104 real changes made, 104 to missing)

{com}. replace deminsure = . if deminsure >= 8
{txt}(0 real changes made)

{com}. recode deminsure (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(deminsure: 4167 changes made)

{com}. label values deminsure insurelab
{txt}
{com}. 
. gen repinsure = V161186
{txt}
{com}. replace repinsure = . if repinsure < 1
{txt}(130 real changes made, 130 to missing)

{com}. replace repinsure = . if repinsure >= 8
{txt}(0 real changes made)

{com}. recode repinsure (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repinsure: 4141 changes made)

{com}. label values repinsure insurelab
{txt}
{com}. 
. gen pdiffinsure = abs(deminsure - repinsure)
{txt}(144 missing values generated)

{com}. 
. 
. * Issue extremity
. gen issextreme = (abs(selfdefense - 0) + abs(selfservice - 0) ///
>         + abs(selfaid - 0) + abs(selfinsure - 0) + abs(selfjobs - 0))/ 5
{txt}(1,385 missing values generated)

{com}. 
. gen issex1 = abs(selfdefense - 0)
{txt}(588 missing values generated)

{com}. gen issex2 = abs(selfservice - 0)       
{txt}(643 missing values generated)

{com}. gen issex3 = abs(selfaid - 0)   
{txt}(517 missing values generated)

{com}. gen issex4 = abs(selfinsure - 0)        
{txt}(505 missing values generated)

{com}. gen issex5 = abs(selfjobs - 0)  
{txt}(497 missing values generated)

{com}.         
. alpha issex1-issex5, gen(issextremealt)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  .325122
{txt}Number of items in the scale:{col 34}{res}        5
{txt}Scale reliability coefficient:{col 34}{res}   0.6543
{txt}
{com}. 
. 
. * Income (recoded into percentiles based on 2016 CPS data)
. gen income = V161361x
{txt}
{com}. replace income = . if V161361x < 1
{txt}(202 real changes made, 202 to missing)

{com}. recode income (1=1) (2=1) (3=1) (4=1) (5=2) (6=2) (7=2) (8=2) ///
>         (9=3) (10=3) (11=3) (12=3) (13=3) (14=3) (15=3) (16=4) (17=4) ///
>         (18=4) (19=4) (20=4) (21=4) (22=4) (23=4) (24=4) (25=4) (26=5) ///
>         (27=5) (28=5)
{txt}(income: 3793 changes made)

{com}. 
. 
. * Education
. gen edu = V161270 
{txt}
{com}. replace edu = . if edu < 1
{txt}(15 real changes made, 15 to missing)

{com}. replace edu = . if edu > 16
{txt}(29 real changes made, 29 to missing)

{com}. recode edu (1=1) (2=1) (3=1) (4=1) (5=2) (6=2) (7=2) (8=2) ///
>         (9=3) (9=3) (10=4) (11=4) (12=4) (13=5) (14=6) (15=6) (16=6)
{txt}(edu: 4226 changes made)

{com}. 
.         
. * Age
. gen age = V161267
{txt}
{com}. replace age = . if V161267 < 18
{txt}(121 real changes made, 121 to missing)

{com}. 
. 
. * Gender
. gen female = V161342 - 1
{txt}
{com}. replace female = . if female > 1
{txt}(11 real changes made, 11 to missing)

{com}. replace female = . if female < 0
{txt}(41 real changes made, 41 to missing)

{com}. 
. 
. * Region 
. gen south = 0
{txt}
{com}. replace south = 1 if V161330 == 1 | V161330 == 5 | V161330 == 12 | V161330 == 13 ///
>         | V161330 == 21 | V161330 == 22 | V161330 == 28 | V161330 == 29 | V161330 == 37 ///
>         | V161330 == 40 | V161330 == 45 | V161330 == 47 | V161330 == 48 | V161330 == 51 ///
>         | V161330 == 54
{txt}(1,272 real changes made)

{com}. 
. 
. keep caseid pid year-south
{txt}
{com}. 
. *save "Value Pol 2016.dta"
. 
. clear
{txt}
{com}. 
. 
. ********************************************************************************
. 
. *** 2012 ANES Cumulative File ***
. 
. use "anes_timeseries_cdf.dta"
{txt}
{com}. 
. ****
. ** Clean/recode variables
. ****
. 
. * Create ID and year identifiers
. gen caseid = VCF0006
{txt}
{com}. 
. gen year = VCF0004
{txt}
{com}. 
. drop if year < 1988
{txt}(31,613 observations deleted)

{com}. drop if year == 1990 | year == 1994 | year == 1998 | year == 2002
{txt}(6,567 observations deleted)

{com}. 
. gen yearalt1 = 0 if year == 1988 
{txt}(15,454 missing values generated)

{com}. replace yearalt1 = 1 if year == 1992
{txt}(2,485 real changes made)

{com}. replace yearalt1 = 2 if year == 1996
{txt}(1,714 real changes made)

{com}. replace yearalt1 = 3 if year == 2000
{txt}(1,807 real changes made)

{com}. replace yearalt1 = 4 if year == 2004
{txt}(1,212 real changes made)

{com}. replace yearalt1 = 5 if year == 2008
{txt}(2,322 real changes made)

{com}. replace yearalt1 = 6 if year == 2012
{txt}(5,914 real changes made)

{com}. 
. gen year1988 = 0
{txt}
{com}. replace year1988 = 1 if year == 1988
{txt}(2,040 real changes made)

{com}. gen year1992 = 0
{txt}
{com}. replace year1992 = 1 if year == 1992
{txt}(2,485 real changes made)

{com}. gen year1996 = 0
{txt}
{com}. replace year1996 = 1 if year == 1996
{txt}(1,714 real changes made)

{com}. gen year2000 = 0
{txt}
{com}. replace year2000 = 1 if year == 2000
{txt}(1,807 real changes made)

{com}. gen year2004 = 0
{txt}
{com}. replace year2004 = 1 if year == 2004
{txt}(1,212 real changes made)

{com}. gen year2008 = 0
{txt}
{com}. replace year2008 = 1 if year == 2008
{txt}(2,322 real changes made)

{com}. gen year2012 = 0
{txt}
{com}. replace year2012 = 1 if year == 2012
{txt}(5,914 real changes made)

{com}. 
. 
. * Survey weights
. gen weight = VCF0009x
{txt}
{com}. 
. 
. * Survey mode
. gen svymode = VCF0017
{txt}
{com}. 
. gen internet = 0
{txt}
{com}. replace internet = 1 if VCF0017 == 4 & year == 2012
{txt}(3,860 real changes made)

{com}.         
.         
. * Self ideology (recoded to range -3-3; -2 Havent thought, -8 DK, -9 NA)
. gen ideo = VCF0803
{txt}
{com}. replace ideo = . if ideo < 1 
{txt}(990 real changes made, 990 to missing)

{com}. replace ideo = . if ideo == 9 
{txt}(3,407 real changes made, 3,407 to missing)

{com}. recode ideo (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(ideo: 13097 changes made)

{com}. label define ideolab -3 "Ex. Liberal" -2 "-2" -1 "-1" 0 "Moderate" ///
>         1 "1" 2 "2" 3 "Ex. Conservative" 
{txt}
{com}. label values ideo ideolab
{txt}
{com}. 
. gen conserv = 1 if ideo > 1
{txt}(10,063 missing values generated)

{com}. replace conserv = 0 if ideo < -1
{txt}(1,927 real changes made)

{com}. 
. 
. * Strength of ideological predisposition
. gen ideostrength = 0 if ideo == 0
{txt}(13,220 missing values generated)

{com}. replace ideostrength = 1 if ideo == -1
{txt}(1,632 real changes made)

{com}. replace ideostrength = 1 if ideo == 1
{txt}(2,230 real changes made)

{com}. replace ideostrength = 2 if ideo == -2
{txt}(1,509 real changes made)

{com}. replace ideostrength = 2 if ideo == 2
{txt}(2,522 real changes made)

{com}. replace ideostrength = 3 if ideo == -3
{txt}(418 real changes made)

{com}. replace ideostrength = 3 if ideo == -3
{txt}(0 real changes made)

{com}. 
. 
. * Self party ID (recoded to range -3-3; -2 DK NA)
. gen pid = VCF0301 
{txt}
{com}. replace pid = . if pid == 0
{txt}(133 real changes made, 133 to missing)

{com}. recode pid (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(pid: 17361 changes made)

{com}. label define partylab -3 "Strong Democrat" -2 "-2" -1 "-1" 0 "Independent" ///
>         1 "1" 2 "2" 3 "Strong Republican"
{txt}
{com}. label values pid partylab
{txt}
{com}. 
. gen rep = 1 if pid > 1 
{txt}(13,099 missing values generated)

{com}. replace rep = 0 if pid < -1
{txt}(6,580 real changes made)

{com}. 
. 
. * Strength of ideological predisposition
. gen pidstrength = 0 if pid == 0
{txt}(15,378 missing values generated)

{com}. replace pidstrength = 1 if pid == -1
{txt}(2,444 real changes made)

{com}. replace pidstrength = 1 if pid == 1
{txt}(1,959 real changes made)

{com}. replace pidstrength = 2 if pid == -2
{txt}(2,838 real changes made)

{com}. replace pidstrength = 2 if pid == 2
{txt}(2,073 real changes made)

{com}. replace pidstrength = 3 if pid == -3
{txt}(3,742 real changes made)

{com}. replace pidstrength = 3 if pid == -3
{txt}(0 real changes made)

{com}. 
. 
. * Education (ranges from 1-7)
. gen edu = VCF0140a
{txt}
{com}. replace edu = . if edu >= 8
{txt}(194 real changes made, 194 to missing)

{com}. label define edulab 1 "8 grades or less" 2 "9-12 grades" 3 "High school" ///
>         4 "HS + non-academic training" 5 "Some college" 6 "BA" 7 "Advanced"
{txt}
{com}. label values edu edulab
{txt}
{com}. 
. 
. * Race 
. gen race = VCF0105a
{txt}
{com}. replace race = . if race == 9
{txt}(123 real changes made, 123 to missing)

{com}. 
. gen black = 1 if race == 2
{txt}(14,773 missing values generated)

{com}. replace black = 0 if race != 2 & race != .
{txt}(14,650 real changes made)

{com}. 
. gen hispanic = 1 if race == 5
{txt}(15,172 missing values generated)

{com}. replace hispanic = 0 if race != 5 & race != .
{txt}(15,049 real changes made)

{com}. 
. 
. * Gender (1=female)
. gen gender = VCF0104
{txt}
{com}. replace gender = . if gender < 1
{txt}(0 real changes made)

{com}. gen female = 1 if gender == 2
{txt}(7,998 missing values generated)

{com}. recode female (.=0)
{txt}(female: 7998 changes made)

{com}. label define genderlab 0 "Male" 1 "Female"
{txt}
{com}. label values female genderlab
{txt}
{com}. 
. 
. * Age (number of years) 
. gen age = VCF0101
{txt}
{com}. replace age = . if age == 00
{txt}(119 real changes made, 119 to missing)

{com}. 
. 
. * Region
. gen south = .
{txt}(17,494 missing values generated)

{com}. replace south = 0 if VCF0112 == 1
{txt}(2,854 real changes made)

{com}. replace south = 0 if VCF0112 == 2
{txt}(4,109 real changes made)

{com}. replace south = 0 if VCF0112 == 4
{txt}(3,842 real changes made)

{com}. replace south = 1 if VCF0112 == 3
{txt}(6,689 real changes made)

{com}. label var south "South Region Dummy"
{txt}
{com}. label define southern 0 "0 Non-South" 1 "1 South"
{txt}
{com}. label values south southern
{txt}
{com}. 
. 
. * Income (1-6, percentiles)
. gen income = VCF0114
{txt}
{com}. replace income = . if income == 0
{txt}(1,348 real changes made, 1,348 to missing)

{com}. 
. 
. * Church attendance
. gen church = VCF0130
{txt}
{com}. replace church = . if church > 5
{txt}(258 real changes made, 258 to missing)

{com}. replace church = . if church < 1
{txt}(24 real changes made, 24 to missing)

{com}. recode church (5=1) (4=2) (3=3) (2=4) (1=5)
{txt}(church: 14877 changes made)

{com}. 
. 
. * Trust (0=Never - 3=Just about always)
. gen trust = VCF0604 - 1
{txt}(2,891 missing values generated)

{com}. replace trust = . if trust > 3
{txt}(75 real changes made, 75 to missing)

{com}. replace trust = . if trust < 0
{txt}(2,386 real changes made, 2,386 to missing)

{com}. 
.  
. * Number of party likes/dislikes (0 - 5 likes)
. gen demlike = VCF0314
{txt}(8,236 missing values generated)

{com}. replace demlike = . if demlike == 9
{txt}(880 real changes made, 880 to missing)

{com}. 
. gen demdislike = VCF0315
{txt}(8,236 missing values generated)

{com}. replace demdislike = . if demdislike == 9
{txt}(880 real changes made, 880 to missing)

{com}. 
. gen replike = VCF0318
{txt}(8,236 missing values generated)

{com}. replace replike = . if replike == 9
{txt}(880 real changes made, 880 to missing)

{com}. 
. gen repdislike = VCF0319
{txt}(8,236 missing values generated)

{com}. replace repdislike = . if repdislike == 9
{txt}(880 real changes made, 880 to missing)

{com}. 
. gen difflike = abs(demlike - replike)
{txt}(9,116 missing values generated)

{com}. 
. gen diffdislike = abs(demdislike - repdislike)
{txt}(9,116 missing values generated)

{com}. 
. 
. * Anger toward president (1=yes)
. gen angerd = VCF0358
{txt}
{com}. replace angerd = . if VCF0358 > 2
{txt}(134 real changes made, 134 to missing)

{com}. replace angerd = . if VCF0358 < 1
{txt}(0 real changes made)

{com}. recode angerd (2=0)
{txt}(angerd: 11153 changes made)

{com}. 
. gen angerr = VCF0370
{txt}
{com}. replace angerr = . if VCF0370 > 2
{txt}(142 real changes made, 142 to missing)

{com}. replace angerr = . if VCF0370 < 1
{txt}(0 real changes made)

{com}. recode angerr (2=0)
{txt}(angerr: 10497 changes made)

{com}. 
. gen anger = angerd if pid < 0
{txt}(8,517 missing values generated)

{com}. replace anger = angerr if pid > 0
{txt}(6,309 real changes made)

{com}. 
. 
. * Perceived difference between parties (1=yes)
. gen perceivediff = VCF0501 - 1
{txt}
{com}. replace perceivediff = . if VCF0501 > 2
{txt}(395 real changes made, 395 to missing)

{com}. replace perceivediff = . if VCF0501 < 1
{txt}(2,529 real changes made, 2,529 to missing)

{com}. 
. 
. * Feeling thermometers
. gen liberaltherm = VCF0211
{txt}
{com}. replace liberaltherm = . if liberaltherm >= 98
{txt}(2,530 real changes made, 2,530 to missing)

{com}. 
. gen conservtherm = VCF0212
{txt}
{com}. replace conservtherm = . if conservtherm >= 98
{txt}(2,460 real changes made, 2,460 to missing)

{com}. 
. gen ideothermdiff = abs(liberaltherm - conservtherm)
{txt}(2,740 missing values generated)

{com}. 
. 
. gen dempartytherm = VCF0218
{txt}
{com}. replace dempartytherm = . if dempartytherm >= 98
{txt}(456 real changes made, 456 to missing)

{com}. 
. gen reppartytherm = VCF0224
{txt}
{com}. replace reppartytherm = . if reppartytherm >= 98
{txt}(479 real changes made, 479 to missing)

{com}. 
. gen partythermdiff = abs(dempartytherm - reppartytherm)
{txt}(526 missing values generated)

{com}. 
. 
. gen demcandtherm = VCF0424
{txt}
{com}. replace demcandtherm = . if demcandtherm >= 98
{txt}(264 real changes made, 264 to missing)

{com}. 
. gen repcandtherm = VCF0426
{txt}
{com}. replace repcandtherm = . if repcandtherm >= 98
{txt}(271 real changes made, 271 to missing)

{com}. 
. gen diffcandtherm = abs(demcandtherm - repcandtherm)
{txt}(395 missing values generated)

{com}. 
. 
. * Party placement on ideological scale
. gen demideo = VCF0503
{txt}
{com}. replace demideo = . if demideo == 0
{txt}(879 real changes made, 879 to missing)

{com}. replace demideo = . if demideo == 8
{txt}(1,221 real changes made, 1,221 to missing)

{com}. recode demideo (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demideo: 15394 changes made)

{com}. label values demideo ideolab
{txt}
{com}. 
. gen repideo = VCF0504
{txt}
{com}. replace repideo = . if repideo == 0
{txt}(890 real changes made, 890 to missing)

{com}. replace repideo = . if repideo == 8
{txt}(1,298 real changes made, 1,298 to missing)

{com}. recode repideo (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repideo: 15306 changes made)

{com}. label values repideo ideolab
{txt}
{com}. 
. gen diffideoplace = abs(demideo - repideo)
{txt}(2,283 missing values generated)

{com}. 
. 
. * Candidate placement on ideological scale
. gen dcandideo = VCF9088
{txt}
{com}. replace dcandideo = . if dcandideo < 1
{txt}(44 real changes made, 44 to missing)

{com}. replace dcandideo = . if dcandideo >= 8
{txt}(2,562 real changes made, 2,562 to missing)

{com}. recode dcandideo (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(dcandideo: 14888 changes made)

{com}. label values dcandideo ideolab
{txt}
{com}. 
. gen rcandideo = VCF9089
{txt}
{com}. replace rcandideo = . if rcandideo < 1
{txt}(610 real changes made, 610 to missing)

{com}. replace rcandideo = . if rcandideo >= 8
{txt}(3,283 real changes made, 3,283 to missing)

{com}. recode rcandideo (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(rcandideo: 13601 changes made)

{com}. label values rcandideo ideolab
{txt}
{com}. 
. gen diffcandideo = abs(dcandideo - rcandideo)
{txt}(5,114 missing values generated)

{com}. 
. 
. * Partisan sorting (ala Mason 2015)
. replace ideostrength = ideostrength + 1
{txt}(12,585 real changes made)

{com}. replace pidstrength = pidstrength + 1
{txt}(15,172 real changes made)

{com}. gen sorting = abs(pid - (-1 * ideo)) * ideostrength * pidstrength
{txt}(6,637 missing values generated)

{com}. 
. 
. * Self and candidate placement on guaranteed jobs scale 
. gen selfjobs = VCF0809
{txt}
{com}. replace selfjobs = . if selfjobs < 1
{txt}(2,005 real changes made, 2,005 to missing)

{com}. replace selfjobs = . if selfjobs >= 8
{txt}(1,535 real changes made, 1,535 to missing)

{com}. recode selfjobs (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfjobs: 13954 changes made)

{com}. label define jobslab -3 "Government see to job and good standard of living" ///
>         3 "Government let each person get ahead on his own"
{txt}
{com}. label values selfjobs jobslab
{txt}
{com}. 
. gen demjobs = VCF9087
{txt}
{com}. replace demjobs = . if demjobs < 1
{txt}(25 real changes made, 25 to missing)

{com}. replace demjobs = . if demjobs >= 8
{txt}(3,849 real changes made, 3,849 to missing)

{com}. recode demjobs (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demjobs: 13620 changes made)

{com}. label values demjobs jobslab
{txt}
{com}. 
. gen repjobs = VCF9095
{txt}
{com}. replace repjobs = . if repjobs < 1
{txt}(25 real changes made, 25 to missing)

{com}. replace repjobs = . if repjobs >= 8
{txt}(3,745 real changes made, 3,745 to missing)

{com}. recode repjobs (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repjobs: 13724 changes made)

{com}. label values repjobs jobslab
{txt}
{com}. 
. gen pdiffjobs = abs(demjobs - repjobs)
{txt}(4,128 missing values generated)

{com}. 
. 
. * Self and candidate placement placement on aid to blacks scale 
. gen selfaid = VCF0830
{txt}
{com}. replace selfaid = . if selfaid < 1
{txt}(931 real changes made, 931 to missing)

{com}. replace selfaid = . if selfaid >= 8
{txt}(1,883 real changes made, 1,883 to missing)

{com}. recode selfaid (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfaid: 14680 changes made)

{com}. label define aidlab -3 "Government should help minority groups" ///
>         3 "Minority groups should help themselves"
{txt}
{com}. label values selfaid aidlab
{txt}
{com}.  
. gen demaid = VCF9084
{txt}(2,485 missing values generated)

{com}. replace demaid = . if demaid < 1
{txt}(259 real changes made, 259 to missing)

{com}. replace demaid = . if demaid >= 8
{txt}(2,379 real changes made, 2,379 to missing)

{com}. recode demaid (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demaid: 12371 changes made)

{com}. label values demaid aidlab
{txt}
{com}. 
. gen repaid = VCF9092
{txt}(2,485 missing values generated)

{com}. replace repaid = . if repaid < 1
{txt}(259 real changes made, 259 to missing)

{com}. replace repaid = . if repaid >= 8
{txt}(2,583 real changes made, 2,583 to missing)

{com}. recode repaid (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repaid: 12167 changes made)

{com}. label values repaid aidlab
{txt}
{com}. 
. gen pdiffaid = abs(demaid - repaid)
{txt}(5,548 missing values generated)

{com}. 
. 
. * Self and candidate placement placement on government services scale 
. gen selfservice = VCF0839
{txt}
{com}. replace selfservice = . if selfservice < 1
{txt}(1,992 real changes made, 1,992 to missing)

{com}. replace selfservice = . if selfservice >= 8
{txt}(2,294 real changes made, 2,294 to missing)

{com}. recode selfservice (1=3) (2=2) (3=1) (4=0) (5=-1) (6=-2) (7=-3)
{txt}(selfservice: 11759 changes made)

{com}. label define servicelab -3 "Government should provide many more services" ///
>         3 "Government should provide many ewer services"
{txt}
{com}. label values selfservice servicelab
{txt}
{com}. 
. gen demservice = VCF9086
{txt}
{com}. replace demservice = . if demservice < 1
{txt}(857 real changes made, 857 to missing)

{com}. replace demservice = . if demservice >= 8
{txt}(3,090 real changes made, 3,090 to missing)

{com}. recode demservice (1=3) (2=2) (3=1) (4=0) (5=-1) (6=-2) (7=-3)
{txt}(demservice: 13134 changes made)

{com}. label values demservice servicelab
{txt}
{com}. 
. gen repservice = VCF9094
{txt}
{com}. replace repservice = . if repservice < 1
{txt}(857 real changes made, 857 to missing)

{com}. replace repservice = . if repservice >= 8
{txt}(3,093 real changes made, 3,093 to missing)

{com}. recode repservice (1=3) (2=2) (3=1) (4=0) (5=-1) (6=-2) (7=-3)
{txt}(repservice: 10551 changes made)

{com}. label values repservice servicelab
{txt}
{com}. 
. gen pdiffservice = abs(demservice - repservice)
{txt}(4,260 missing values generated)

{com}. 
. 
. * Self and candidate placement placement on defense spending scale 
. gen selfdefense = VCF0843
{txt}
{com}. replace selfdefense = . if selfdefense < 1
{txt}(1,994 real changes made, 1,994 to missing)

{com}. replace selfdefense = . if selfdefense >= 8
{txt}(2,075 real changes made, 2,075 to missing)

{com}. recode selfdefense (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfdefense: 13425 changes made)

{com}. label define defenselab -3 "Greatly decrease defense spending" ///
>         3 "Greatly increase defense spending"
{txt}
{com}. label values selfdefense defenselab
{txt}
{com}. 
. gen demdefense = VCF9081
{txt}
{com}. replace demdefense = . if demdefense < 1
{txt}(610 real changes made, 610 to missing)

{com}. replace demdefense = . if demdefense >= 8
{txt}(3,456 real changes made, 3,456 to missing)

{com}. recode demdefense (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demdefense: 13428 changes made)

{com}. label values demdefense defenselab
{txt}
{com}. 
. gen repdefense = VCF9089
{txt}
{com}. replace repdefense = . if repdefense < 1
{txt}(610 real changes made, 610 to missing)

{com}. replace repdefense = . if repdefense >= 8
{txt}(3,283 real changes made, 3,283 to missing)

{com}. recode repdefense (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repdefense: 13601 changes made)

{com}. label values repdefense defenselab
{txt}
{com}. 
. gen pdiffdefense = abs(demdefense - repdefense)
{txt}(4,399 missing values generated)

{com}. 
. 
. * Self and candidate placement on government health insurance scale
. gen selfinsure = VCF0806
{txt}
{com}. replace selfinsure = . if selfinsure < 1
{txt}(1,998 real changes made, 1,998 to missing)

{com}. replace selfinsure = . if selfinsure >= 8
{txt}(1,551 real changes made, 1,551 to missing)

{com}. recode selfinsure (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfinsure: 13945 changes made)

{com}. label define insurelab -3 "Government insurance plan" 3 "Private insurance plan"
{txt}
{com}. label values selfinsure insurelab
{txt}
{com}. 
. gen deminsure = VCF9085
{txt}(5,504 missing values generated)

{com}. replace deminsure = . if deminsure < 1
{txt}(12 real changes made, 12 to missing)

{com}. replace deminsure = . if deminsure >= 8
{txt}(2,221 real changes made, 2,221 to missing)

{com}. recode deminsure (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(deminsure: 9757 changes made)

{com}. label values deminsure insurelab
{txt}
{com}. 
. gen repinsure = VCF9093
{txt}(5,504 missing values generated)

{com}. replace repinsure = . if repinsure < 1
{txt}(12 real changes made, 12 to missing)

{com}. replace repinsure = . if repinsure >= 8
{txt}(2,419 real changes made, 2,419 to missing)

{com}. recode repinsure (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repinsure: 9559 changes made)

{com}. label values repinsure insurelab
{txt}
{com}. 
. gen pdiffinsure = abs(deminsure - repinsure)
{txt}(8,077 missing values generated)

{com}. 
. 
. * Issue extremity
. gen issextreme = (abs(selfdefense - 0) + abs(selfservice - 0) ///
>         + abs(selfaid - 0) + abs(selfinsure - 0) + abs(selfjobs - 0))/ 5
{txt}(7,175 missing values generated)

{com}. 
. gen issex1 = abs(selfdefense - 0)
{txt}(4,069 missing values generated)

{com}. gen issex2 = abs(selfservice - 0)       
{txt}(4,286 missing values generated)

{com}. gen issex3 = abs(selfaid - 0)   
{txt}(2,814 missing values generated)

{com}. gen issex4 = abs(selfinsure - 0)        
{txt}(3,549 missing values generated)

{com}. gen issex5 = abs(selfjobs - 0)  
{txt}(3,540 missing values generated)

{com}.         
. alpha issex1-issex5, gen(issextremealt)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .2972098
{txt}Number of items in the scale:{col 34}{res}        5
{txt}Scale reliability coefficient:{col 34}{res}   0.6292
{txt}
{com}. 
. 
. * Do whatever is necessary for equal opportunity (reverse coded)
. gen equalopp = VCF9013 - 1
{txt}
{com}. replace equalopp = . if equalopp > 4
{txt}(1,754 real changes made, 1,754 to missing)

{com}. 
. 
. * Have gone too far pushing equal rights (reverse coded)
. gen equalrights = VCF9014
{txt}
{com}. replace equalrights = . if equalrights > 5
{txt}(1,797 real changes made, 1,797 to missing)

{com}. recode equalrights (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(equalrights: 15697 changes made)

{com}. 
. 
. * Big problem is not giving everyone an equal chance
. gen equalchance = VCF9015 - 1
{txt}
{com}. replace equalchance = . if equalchance > 4
{txt}(1,779 real changes made, 1,779 to missing)

{com}. 
. 
. * Better off if we worried less about equality (reverse coded)
. gen lessequal = VCF9017
{txt}
{com}. replace lessequal = . if lessequal > 5
{txt}(1,822 real changes made, 1,822 to missing)

{com}. recode lessequal (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(lessequal: 15672 changes made)

{com}. 
. 
. * Not that big of a problem if people have more of a chance (reverse coded)
. gen unequal = VCF9016
{txt}
{com}. replace unequal = . if unequal > 5
{txt}(1,815 real changes made, 1,815 to missing)

{com}. recode unequal (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(unequal: 15679 changes made)

{com}. 
. 
. * Many fewer problems if people were treated equally
. gen fewer = VCF9018 - 1
{txt}
{com}. replace fewer = . if fewer > 4
{txt}(1,786 real changes made, 1,786 to missing)

{com}. 
. 
. * Adjusting views of moral behavior
. gen changing = VCF0852 - 1
{txt}
{com}. replace changing = . if changing > 4
{txt}(1,778 real changes made, 1,778 to missing)

{com}.  
.  
. * Newer lifestyles contributing to a breakdown in society (reverse coded)
. gen lifestyles = VCF0851
{txt}
{com}. replace lifestyles = . if lifestyles > 5
{txt}(1,834 real changes made, 1,834 to missing)

{com}. recode lifestyles (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(lifestyles: 15660 changes made)

{com}. 
. 
. * Tolerant of people who choose to live according to their own moral standards
. gen standards = VCF0854 - 1
{txt}
{com}. replace standards = . if standards > 4
{txt}(1,809 real changes made, 1,809 to missing)

{com}.  
.  
. * More emphasis on traditional family ties (reverse coded)
. gen family = VCF0853
{txt}
{com}. replace family = . if family > 5
{txt}(1,790 real changes made, 1,790 to missing)

{com}. recode family (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(family: 15704 changes made)

{com}. 
. 
. * Creating values scales
. factor equalopp equalchance lessequal fewer changing lifestyles standards ///
>         family, ipf
{txt}(obs=15,463)

Factor analysis/correlation{col 50}Number of obs    = {res}    15,463
{col 5}{txt}Method: iterated principal factors{col 50}Retained factors =   {res}       7
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      28

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.02711      0.97051            0.5366       0.5366
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.05660      0.59982            0.2797       0.8164
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.45678      0.31528            0.1209       0.9373
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.14149      0.08419            0.0375       0.9747
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.05731      0.01985            0.0152       0.9899
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}      0.03746      0.03659            0.0099       0.9998
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}      0.00087      0.00109            0.0002       1.0001
{txt}{col 5}{ralign 11:Factor8}  {c |}{res}     -0.00022            .           -0.0001       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}28{txt}) ={res} 2.2e+04{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{space 1}{ralign 8:Factor4}{space 1}{space 1}{ralign 8:Factor5}{space 1}{space 1}{ralign 8:Factor6}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:equalopp}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4473}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3467}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0501}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1743}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0681}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1084}}}{space 1}
{space 4}{space 0}{ralign 12:equalchance}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5785}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3851}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1053}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1681}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0411}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0664}}}{space 1}
{space 4}{space 0}{ralign 12:lessequal}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4146}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0164}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3146}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2073}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0337}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0768}}}{space 1}
{space 4}{space 0}{ralign 12:fewer}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5579}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4466}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0694}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1010}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1138}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0306}}}{space 1}
{space 4}{space 0}{ralign 12:changing}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5247}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0834}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4222}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0317}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0975}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0325}}}{space 1}
{space 4}{space 0}{ralign 12:lifestyles}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4896}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5260}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1272}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0058}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1095}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0762}}}{space 1}
{space 4}{space 0}{ralign 12:standards}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5323}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1378}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3335}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1201}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0835}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0869}}}{space 1}
{space 4}{space 0}{ralign 12:family}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4594}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5345}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1834}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1191}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0918}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0073}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor7}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:equalopp}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0063}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6304}}}{space 1}
{space 4}{space 0}{ralign 12:equalchance}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0165}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4713}}}{space 1}
{space 4}{space 0}{ralign 12:lessequal}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0073}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6788}}}{space 1}
{space 4}{space 0}{ralign 12:fewer}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0167}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4602}}}{space 1}
{space 4}{space 0}{ralign 12:changing}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0063}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5278}}}{space 1}
{space 4}{space 0}{ralign 12:lifestyles}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0105}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4495}}}{space 1}
{space 4}{space 0}{ralign 12:standards}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0034}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5575}}}{space 1}
{space 4}{space 0}{ralign 12:family}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0079}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4468}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. 
. alpha equalopp equalchance lessequal fewer changing lifestyles standards ///
>         family, detail item generate(values) casewise

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
equalopp{col 14}{c |}{res}{col 16}15463{col 24}+{col 31} 0.5041{col 45} 0.3505{col 59} .3535878{col 73} 0.6687
{txt}equalchance{col 14}{c |}{res}{col 16}15463{col 24}+{col 31} 0.6127{col 45} 0.4307{col 59} .3095431{col 73} 0.6491
{txt}lessequal{col 14}{c |}{res}{col 16}15463{col 24}+{col 31} 0.5389{col 45} 0.3324{col 59} .3341807{col 73} 0.6736
{txt}fewer{col 14}{c |}{res}{col 16}15463{col 24}+{col 31} 0.5794{col 45} 0.4089{col 59} .3248781{col 73} 0.6550
{txt}changing{col 14}{c |}{res}{col 16}15463{col 24}+{col 31} 0.6016{col 45} 0.3953{col 59} .3098049{col 73} 0.6588
{txt}lifestyles{col 14}{c |}{res}{col 16}15463{col 24}+{col 31} 0.5502{col 45} 0.3661{col 59} .3327169{col 73} 0.6646
{txt}standards{col 14}{c |}{res}{col 16}15463{col 24}+{col 31} 0.5976{col 45} 0.4274{col 59} .3182433{col 73} 0.6506
{txt}family{col 14}{c |}{res}{col 16}15463{col 24}+{col 31} 0.5136{col 45} 0.3440{col 59} .3475259{col 73} 0.6693
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .3288101{col 73} 0.6906
{txt}{hline 13}{c BT}{hline 65}

Interitem covariances (obs=15463 in all pairs)

                equalopp  equalchance    lessequal        fewer     changing
   equalopp  {res}     0.9971
{txt}equalchance  {res}     0.4723       1.6893
{txt}  lessequal  {res}     0.3009       0.4134       1.7834
{txt}      fewer  {res}     0.4555       0.7872       0.3457       1.3992
{txt}   changing  {res}     0.2689       0.4329       0.1525       0.3693       2.0256
{txt} lifestyles  {res}     0.0524       0.1362       0.3936       0.0901       0.4162
{txt}  standards  {res}     0.2160       0.3165       0.2363       0.2951       0.7154
{txt}     family  {res}     0.0153       0.1478       0.3466       0.0414       0.3456

             {txt} lifestyles    standards       family
 lifestyles  {res}     1.4985
{txt}  standards  {res}     0.4318       1.4542
{txt}     family  {res}     0.6994       0.3125       1.2096
{txt}
{com}.         
. sem (Values -> equalopp-family), method(mlmv) latent(Values) nocapslatent
{res}{txt}(1721 all-missing observations excluded)

Endogenous variables

{p 0 14 2}Measurement:{space 2}{res}equalopp equalrights equalchance lessequal unequal fewer changing lifestyles standards family{p_end}
{txt}
Exogenous variables

{p 0 14 2}Latent:{space 7}{res}Values{p_end}
{txt}
Fitting saturated model:

Iteration 0:{space 3}log likelihood = {res:-238556.05}  
Iteration 1:{space 3}log likelihood = {res:-238554.54}  
Iteration 2:{space 3}log likelihood = {res:-238554.54}  

Fitting baseline model:

Iteration 0:{space 3}log likelihood = {res:-255170.48}  
Iteration 1:{space 3}log likelihood = {res:-255170.46}  
{res}{txt}
Fitting target model:

Iteration 0:{space 3}log likelihood = {res:-246437.67}  
Iteration 1:{space 3}log likelihood = {res:-245559.53}  
Iteration 2:{space 3}log likelihood = {res:-245396.65}  
Iteration 3:{space 3}log likelihood = {res:-245318.07}  
Iteration 4:{space 3}log likelihood = {res:-245310.48}  
Iteration 5:{space 3}log likelihood = {res:-245310.27}  
Iteration 6:{space 3}log likelihood = {res:-245310.27}  

{col 1}Structural equation model{col 49}Number of obs{col 67}= {res}    15,773
{txt}{col 1}Estimation method{col 20}= {res}mlmv
{txt}{col 1}Log likelihood{col 20}= {res}-245310.27

{p 0 7}{space 1}{text:( 1)}{space 1} [equalopp]Values = 1{p_end}
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}      OIM
{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Measurement    {col 17}{txt}{c |}
{space 2}{col 3}equalopp     {col 17}{c |}
{space 9}Values {c |}{col 17}{res}{space 2}        1{col 29}{txt}  (constrained)
{space 10}_cons {c |}{col 17}{res}{space 2} .6984206{col 29}{space 2} .0079537{col 40}{space 1}   87.81{col 49}{space 3}0.000{col 57}{space 4} .6828317{col 70}{space 3} .7140096
{space 2}{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}equalrights  {col 17}{c |}
{space 9}Values {c |}{col 17}{res}{space 2} 2.123785{col 29}{space 2} .0547707{col 40}{space 1}   38.78{col 49}{space 3}0.000{col 57}{space 4} 2.016436{col 70}{space 3} 2.231133
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.913748{col 29}{space 2} .0108015{col 40}{space 1}  177.18{col 49}{space 3}0.000{col 57}{space 4} 1.892577{col 70}{space 3} 1.934918
{space 2}{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}equalchance  {col 17}{c |}
{space 9}Values {c |}{col 17}{res}{space 2} 1.529663{col 29}{space 2} .0391455{col 40}{space 1}   39.08{col 49}{space 3}0.000{col 57}{space 4} 1.452939{col 70}{space 3} 1.606386
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.682784{col 29}{space 2} .0103685{col 40}{space 1}  162.30{col 49}{space 3}0.000{col 57}{space 4} 1.662462{col 70}{space 3} 1.703106
{space 2}{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}lessequal    {col 17}{c |}
{space 9}Values {c |}{col 17}{res}{space 2} 2.073331{col 29}{space 2} .0537494{col 40}{space 1}   38.57{col 49}{space 3}0.000{col 57}{space 4} 1.967984{col 70}{space 3} 2.178678
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.029468{col 29}{space 2} .0106609{col 40}{space 1}  190.37{col 49}{space 3}0.000{col 57}{space 4} 2.008573{col 70}{space 3} 2.050363
{space 2}{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}unequal      {col 17}{c |}
{space 9}Values {c |}{col 17}{res}{space 2} 1.559646{col 29}{space 2} .0424858{col 40}{space 1}   36.71{col 49}{space 3}0.000{col 57}{space 4} 1.476375{col 70}{space 3} 1.642916
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.662506{col 29}{space 2} .0098383{col 40}{space 1}  168.98{col 49}{space 3}0.000{col 57}{space 4} 1.643223{col 70}{space 3} 1.681789
{space 2}{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}fewer        {col 17}{c |}
{space 9}Values {c |}{col 17}{res}{space 2} 1.329275{col 29}{space 2}   .03464{col 40}{space 1}   38.37{col 49}{space 3}0.000{col 57}{space 4} 1.261381{col 70}{space 3} 1.397168
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.299475{col 29}{space 2} .0094346{col 40}{space 1}  137.74{col 49}{space 3}0.000{col 57}{space 4} 1.280984{col 70}{space 3} 1.317967
{space 2}{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}changing     {col 17}{c |}
{space 9}Values {c |}{col 17}{res}{space 2} 1.105673{col 29}{space 2} .0383521{col 40}{space 1}   28.83{col 49}{space 3}0.000{col 57}{space 4} 1.030505{col 70}{space 3} 1.180842
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.068011{col 29}{space 2} .0113564{col 40}{space 1}  182.10{col 49}{space 3}0.000{col 57}{space 4} 2.045753{col 70}{space 3} 2.090269
{space 2}{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}lifestyles   {col 17}{c |}
{space 9}Values {c |}{col 17}{res}{space 2} 1.238775{col 29}{space 2} .0391012{col 40}{space 1}   31.68{col 49}{space 3}0.000{col 57}{space 4} 1.162138{col 70}{space 3} 1.315412
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.668946{col 29}{space 2} .0097735{col 40}{space 1}  273.08{col 49}{space 3}0.000{col 57}{space 4}  2.64979{col 70}{space 3} 2.688102
{space 2}{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}standards    {col 17}{c |}
{space 9}Values {c |}{col 17}{res}{space 2} 1.126777{col 29}{space 2} .0347646{col 40}{space 1}   32.41{col 49}{space 3}0.000{col 57}{space 4} 1.058639{col 70}{space 3} 1.194914
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.504472{col 29}{space 2} .0096248{col 40}{space 1}  156.31{col 49}{space 3}0.000{col 57}{space 4} 1.485608{col 70}{space 3} 1.523336
{space 2}{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}family       {col 17}{c |}
{space 9}Values {c |}{col 17}{res}{space 2} 1.035052{col 29}{space 2} .0341115{col 40}{space 1}   30.34{col 49}{space 3}0.000{col 57}{space 4} .9681948{col 70}{space 3} 1.101909
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 3.043776{col 29}{space 2} .0087621{col 40}{space 1}  347.38{col 49}{space 3}0.000{col 57}{space 4} 3.026602{col 70}{space 3} 3.060949
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}var(e.equalopp){c |}{col 17}{res}{space 2} .8261678{col 29}{space 2} .0101709{col 57}{space 4} .8064718{col 70}{space 3} .8463449
{txt}var(e.equalri~s){c |}{col 17}{res}{space 2} 1.067734{col 29}{space 2} .0165327{col 57}{space 4} 1.035817{col 70}{space 3} 1.100634
{txt}var(e.equalch~e){c |}{col 17}{res}{space 2} 1.293003{col 29}{space 2} .0169955{col 57}{space 4} 1.260118{col 70}{space 3} 1.326747
{txt}var(e.lessequal){c |}{col 17}{res}{space 2} 1.054189{col 29}{space 2} .0164948{col 57}{space 4}  1.02235{col 70}{space 3} 1.087019
{txt}{space 2}var(e.unequal){c |}{col 17}{res}{space 2} 1.106158{col 29}{space 2} .0144832{col 57}{space 4} 1.078133{col 70}{space 3} 1.134912
{txt}{space 4}var(e.fewer){c |}{col 17}{res}{space 2} 1.098926{col 29}{space 2} .0142613{col 57}{space 4} 1.071327{col 70}{space 3} 1.127236
{txt}{space 1}var(e.changing){c |}{col 17}{res}{space 2} 1.819762{col 29}{space 2} .0216655{col 57}{space 4}  1.77779{col 70}{space 3} 1.862724
{txt}var(e.lifesty~s){c |}{col 17}{res}{space 2} 1.236534{col 29}{space 2} .0152833{col 57}{space 4} 1.206939{col 70}{space 3} 1.266854
{txt}var(e.standards){c |}{col 17}{res}{space 2} 1.238247{col 29}{space 2} .0150628{col 57}{space 4} 1.209074{col 70}{space 3} 1.268125
{txt}{space 3}var(e.family){c |}{col 17}{res}{space 2} 1.024265{col 29}{space 2}  .012451{col 57}{space 4}  1.00015{col 70}{space 3} 1.048962
{txt}{space 5}var(Values){c |}{col 17}{res}{space 2} .1697784{col 29}{space 2} .0074842{col 57}{space 4} .1557256{col 70}{space 3} .1850994
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
LR test of model vs. saturated: chi2({res:35})  = {res: 13511.45}, Prob > chi2 = {res}0.0000
{txt}
{com}. predict valuefac, latent        
{txt}{p 0 1 2}(latent(Values) assumed){p_end}
{res}{txt}
{com}. 
. alpha equalopp equalchance lessequal fewer, ///
>         detail item generate(equality) casewise

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
equalopp{col 14}{c |}{res}{col 16}15599{col 24}+{col 31} 0.6593{col 45} 0.4353{col 59} .5134639{col 73} 0.5812
{txt}equalchance{col 14}{c |}{res}{col 16}15599{col 24}+{col 31} 0.7652{col 45} 0.5087{col 59} .3658843{col 73} 0.5166
{txt}lessequal{col 14}{c |}{res}{col 16}15599{col 24}+{col 31} 0.6297{col 45} 0.2886{col 59} .5702016{col 73} 0.6837
{txt}fewer{col 14}{c |}{res}{col 16}15599{col 24}+{col 31} 0.7465{col 45} 0.5117{col 59} .3946795{col 73} 0.5195
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .4610573{col 73} 0.6471
{txt}{hline 13}{c BT}{hline 65}

Interitem covariances (obs=15599 in all pairs)

                equalopp  equalchance    lessequal        fewer
   equalopp  {res}     0.9976
{txt}equalchance  {res}     0.4716       1.6883
{txt}  lessequal  {res}     0.3009       0.4116       1.7834
{txt}      fewer  {res}     0.4535       0.7855       0.3433       1.3984
{txt}
{com}. 
. alpha changing lifestyles standards family, detail item ///
>         generate(morality) casewise

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
changing{col 14}{c |}{res}{col 16}15570{col 24}+{col 31} 0.7098{col 45} 0.3908{col 59} .4797829{col 73} 0.6136
{txt}lifestyles{col 14}{c |}{res}{col 16}15570{col 24}+{col 31} 0.7171{col 45} 0.4632{col 59} .4569234{col 73} 0.5534
{txt}standards{col 14}{c |}{res}{col 16}15570{col 24}+{col 31} 0.6965{col 45} 0.4368{col 59} .4860539{col 73} 0.5721
{txt}family{col 14}{c |}{res}{col 16}15570{col 24}+{col 31} 0.6722{col 45} 0.4327{col 59} .5207532{col 73} 0.5784
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .4858784{col 73} 0.6470
{txt}{hline 13}{c BT}{hline 65}

Interitem covariances (obs=15570 in all pairs)

              changing  lifestyles   standards      family
  changing  {res}    2.0272
{txt}lifestyles  {res}    0.4161      1.4964
{txt} standards  {res}    0.7152      0.4310      1.4552
{txt}    family  {res}    0.3446      0.6974      0.3109      1.2069
{txt}
{com}.         
. gen valuediff = equality - morality     
{txt}(2,031 missing values generated)

{com}. gen absvaluediff = abs(valuediff)
{txt}(2,031 missing values generated)

{com}. 
. 
. * Vote choice
. gen prezvote = VCF0704
{txt}
{com}. replace prezvote = . if prezvote == 0
{txt}(5,863 real changes made, 5,863 to missing)

{com}. label define prezvotelab 1 "Democrat" 2 "Republican" 3 "Third party"
{txt}
{com}. label values prez prezvotelab
{txt}
{com}. 
. gen congvote = VCF0707
{txt}
{com}. replace congvote = . if congvote == 0
{txt}(7,532 real changes made, 7,532 to missing)

{com}. label define votelab 1 "Democrat" 2 "Republican"
{txt}
{com}. label values congvote votelab
{txt}
{com}. 
. gen senvote = VCF0708
{txt}
{com}. replace senvote = . if senvote == 0
{txt}(10,086 real changes made, 10,086 to missing)

{com}. label values senvote votelab
{txt}
{com}. 
. 
. * Level of information, as assessed by interviewer
. gen info = VCF0050a
{txt}(3,883 missing values generated)

{com}. replace info = . if info == 9
{txt}(116 real changes made, 116 to missing)

{com}. recode info (1=5) (2=4) (3=3) (4=2) (1=5)
{txt}(info: 8197 changes made)

{com}. label define infolab 1 "Very low" 2 "Fairly Low" 3 "Average" ///
>         4 "Fairly High" 5 "Very High"
{txt}
{com}. label values info infolab
{txt}
{com}. 
. 
. * Interest in campaigns (1 Hardly at all - 4 Most of the time)
. gen interest = VCF0310
{txt}
{com}. replace interest = . if VCF0310 == 0
{txt}(1,162 real changes made, 1,162 to missing)

{com}. replace interest = . if VCF0310 == 9
{txt}(1 real change made, 1 to missing)

{com}. label var interest "Interest in the the Campaigns"
{txt}
{com}. label define interestlab 1 "Not much interested" ///
>         2 "Somewhat interested" 3 "Very much interested"
{txt}
{com}. label values interest interestlab
{txt}
{com}. 
. 
. * External efficacy index (0 Least efficacious - 100 Most efficacious)
. gen efficacy = VCF0648
{txt}(2,891 missing values generated)

{com}. replace efficacy = . if efficacy == 999
{txt}(2,215 real changes made, 2,215 to missing)

{com}. 
. 
. * Participation index (1 Lowest - 6 Highest)
. gen participate = VCF0723
{txt}
{com}. replace participate = . if participate == 0
{txt}(1,699 real changes made, 1,699 to missing)

{com}. 
. 
. * Media exposure index (1 No media - 5 All four media)
. gen media = VCF0728
{txt}(12,083 missing values generated)

{com}. replace media = . if media == 0
{txt}(114 real changes made, 114 to missing)

{com}. 
. 
. * Sophistication scale (all years but 1994)
. factor info participate, factor(1) ipf
{txt}(obs=12,101)

Factor analysis/correlation{col 50}Number of obs    = {res}    12,101
{col 5}{txt}Method: iterated principal factors{col 50}Retained factors =   {res}       1
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       1

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.46494      0.46503            1.0002       1.0002
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.00009            .           -0.0002       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}1{txt})  ={res}  672.48{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:info}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4822}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7675}}}{space 1}
{space 4}{space 0}{ralign 12:participate}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4822}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7675}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. predict sophscale
{txt}(regression scoring assumed)

{p 0 0 2}Scoring coefficients (method = regression){p_end}

{space 4}{hline 13}{c  TT}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}
{space 4}{hline 13}{c   +}{hline 10}
{space 4}{space 0}{ralign 12:info}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.39119}}}{space 1}
{space 4}{space 0}{ralign 12:participate}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.39119}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}


{com}. 
. gen sophscale94 = interest + participate
{txt}(2,756 missing values generated)

{com}. replace sophscale = sophscale94 if year == 1994
{txt}(0 real changes made)

{com}. 
. 
. * Difference in party mean DW-NOMINATE scores
. gen housepol = 0.492 if year == 1972
{txt}(17,494 missing values generated)

{com}. replace housepol = 0.499 if year == 1976
{txt}(0 real changes made)

{com}. replace housepol = 0.489 if year == 1978
{txt}(0 real changes made)

{com}. replace housepol = 0.514 if year == 1980
{txt}(0 real changes made)

{com}. replace housepol = 0.540 if year == 1982
{txt}(0 real changes made)

{com}. replace housepol = 0.573 if year == 1984
{txt}(0 real changes made)

{com}. replace housepol = 0.618 if year == 1988
{txt}(2,040 real changes made)

{com}. replace housepol = 0.634 if year == 1990
{txt}(0 real changes made)

{com}. replace housepol = 0.664 if year == 1992
{txt}(2,485 real changes made)

{com}. replace housepol = 0.727 if year == 1994
{txt}(0 real changes made)

{com}. replace housepol = 0.818 if year == 1996
{txt}(1,714 real changes made)

{com}. replace housepol = 0.877 if year == 2000
{txt}(1,807 real changes made)

{com}. replace housepol = 0.942 if year == 2004
{txt}(1,212 real changes made)

{com}. replace housepol = 0.994 if year == 2008
{txt}(2,322 real changes made)

{com}. replace housepol = 1.065 if year == 2012
{txt}(5,914 real changes made)

{com}. replace housepol = 1.076 if year == 2016
{txt}(0 real changes made)

{com}. 
. keep caseid-housepol
{txt}
{com}. 
. 
. append using "Value Pol 2016.dta"
{txt}(label insurelab already defined)
(label defenselab already defined)
(label servicelab already defined)
(label aidlab already defined)
(label jobslab already defined)

{com}. 
. 
. ********************************************************************************
. 
. ****
. ** Analyses
. ****
. 
. * Generate value polarization variable
. gen valuepold = .
{txt}(21,765 missing values generated)

{com}. gen valuepolr = .       
{txt}(21,765 missing values generated)

{com}.         
. sum values if values != . & rep == 1, meanonly
{txt}
{com}. replace valuepold = values - r(mean) if values != . & rep == 0
{txt}(7,486 real changes made)

{com}. sum values if values != . & rep == 0, meanonly
{txt}
{com}. replace valuepolr = values - r(mean) if values != . & rep == 1
{txt}(5,416 real changes made)

{com}. 
. gen valuepoldabs = abs(valuepold)
{txt}(14,279 missing values generated)

{com}. gen valuepolrabs = abs(valuepolr)
{txt}(16,349 missing values generated)

{com}. egen valuepol = rowmax(valuepoldabs valuepolrabs)
{txt}(8863 missing values generated)

{com}. 
. 
. * "Raw" value polarization (R1's request)
. sum values if values != . & rep == 1, meanonly
{txt}
{com}. gen valuepoldalt = r(mean) - values if values != . & rep == 0
{txt}(14,279 missing values generated)

{com}. egen valuepolalt = rowmax(valuepoldalt valuepolr)
{txt}(8863 missing values generated)

{com}. 
. 
. * Intra-party value extremity (R1's request)
. sum values if values != . & rep == 1, meanonly
{txt}
{com}. gen intravaluer = values - r(mean) if values != . & rep == 1
{txt}(16,349 missing values generated)

{com}. sum values if values != . & rep == 0, meanonly
{txt}
{com}. gen intravalued = r(mean) - values if values != . & rep == 0
{txt}(14,279 missing values generated)

{com}. 
. egen valueextreme = rowmax(intravaluer intravalued)
{txt}(8863 missing values generated)

{com}. 
. 
. * Rescale 0-1
. foreach v of var valuepol partythermdiff ideothermdiff difflike diffdislike ///
> diffcandtherm diffideoplace diffcandideo sorting edu income church ///
> housepol age interest issextreme issextremealt ///
> pidstrength ideostrength valuepolalt valueextreme{c -(} 
{txt}  2{com}.         su `v', meanonly 
{txt}  3{com}.         gen `v'2 = (`v' - r(min))/(r(max) - r(min)) 
{txt}  4{com}. {c )-}
{txt}(8,863 missing values generated)
(621 missing values generated)
(3,445 missing values generated)
(13,387 missing values generated)
(13,387 missing values generated)
(457 missing values generated)
(2,396 missing values generated)
(5,305 missing values generated)
(7,865 missing values generated)
(238 missing values generated)
(1,550 missing values generated)
(2,004 missing values generated)
(4,271 missing values generated)
(240 missing values generated)
(1,163 missing values generated)
(8,560 missing values generated)
(1,360 missing values generated)
(2,345 missing values generated)
(6,130 missing values generated)
(8,863 missing values generated)
(8,863 missing values generated)

{com}.    
. ****
. ** Table 1 estimates
. ****
. 
. reg partythermdiff2 valuepol2 sorting2 issextremealt2 interest2 church2 ///
>         income2 edu2 age2 female black hispanic south i.year, beta

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     6,231
{txt}{hline 13}{c +}{hline 34}   F(19, 6211)     = {res}   106.24
{txt}       Model {c |} {res}  125.83982        19  6.62314843   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 387.220369     6,211  .062344287   {txt}R-squared       ={res}    0.2453
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2430
{txt}       Total {c |} {res} 513.060189     6,230   .08235316   {txt}Root MSE        =   {res} .24969

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}partythermdi~2{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 69}        Beta
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}valuepol2 {c |}{col 16}{res}{space 2} .1333511{col 28}{space 2}  .016195{col 39}{space 1}    8.23{col 48}{space 3}0.000{col 69}{space 3} .0999281
{txt}{space 6}sorting2 {c |}{col 16}{res}{space 2}  .342431{col 28}{space 2} .0141072{col 39}{space 1}   24.27{col 48}{space 3}0.000{col 69}{space 3} .2949046
{txt}issextremealt2 {c |}{col 16}{res}{space 2} .1009107{col 28}{space 2} .0139867{col 39}{space 1}    7.21{col 48}{space 3}0.000{col 69}{space 3} .0827659
{txt}{space 5}interest2 {c |}{col 16}{res}{space 2} .1440496{col 28}{space 2} .0165233{col 39}{space 1}    8.72{col 48}{space 3}0.000{col 69}{space 3} .1214282
{txt}{space 7}church2 {c |}{col 16}{res}{space 2}-.0309283{col 28}{space 2} .0087416{col 39}{space 1}   -3.54{col 48}{space 3}0.000{col 69}{space 3}-.0415713
{txt}{space 7}income2 {c |}{col 16}{res}{space 2}-.0310277{col 28}{space 2} .0125527{col 39}{space 1}   -2.47{col 48}{space 3}0.013{col 69}{space 3}-.0309558
{txt}{space 10}edu2 {c |}{col 16}{res}{space 2}-.0577626{col 28}{space 2} .0139668{col 39}{space 1}   -4.14{col 48}{space 3}0.000{col 69}{space 3}-.0518264
{txt}{space 10}age2 {c |}{col 16}{res}{space 2} .0766896{col 28}{space 2} .0162279{col 39}{space 1}    4.73{col 48}{space 3}0.000{col 69}{space 3} .0551061
{txt}{space 8}female {c |}{col 16}{res}{space 2} .0360683{col 28}{space 2} .0064351{col 39}{space 1}    5.60{col 48}{space 3}0.000{col 69}{space 3} .0626509
{txt}{space 9}black {c |}{col 16}{res}{space 2} .1283116{col 28}{space 2} .0093197{col 39}{space 1}   13.77{col 48}{space 3}0.000{col 69}{space 3} .1650564
{txt}{space 6}hispanic {c |}{col 16}{res}{space 2} .0617949{col 28}{space 2} .0102653{col 39}{space 1}    6.02{col 48}{space 3}0.000{col 69}{space 3} .0703942
{txt}{space 9}south {c |}{col 16}{res}{space 2}-.0029565{col 28}{space 2} .0068218{col 39}{space 1}   -0.43{col 48}{space 3}0.665{col 69}{space 3}-.0049134
{txt}{space 14} {c |}
{space 10}year {c |}
{space 9}1992  {c |}{col 16}{res}{space 2}-.0341717{col 28}{space 2} .0150207{col 39}{space 1}   -2.27{col 48}{space 3}0.023{col 69}{space 3}-.0375823
{txt}{space 9}1996  {c |}{col 16}{res}{space 2} .0117812{col 28}{space 2} .0157678{col 39}{space 1}    0.75{col 48}{space 3}0.455{col 69}{space 3} .0117233
{txt}{space 9}2000  {c |}{col 16}{res}{space 2} .0610761{col 28}{space 2} .0262194{col 39}{space 1}    2.33{col 48}{space 3}0.020{col 69}{space 3} .0285253
{txt}{space 9}2004  {c |}{col 16}{res}{space 2} .0326216{col 28}{space 2} .0182169{col 39}{space 1}    1.79{col 48}{space 3}0.073{col 69}{space 3} .0252773
{txt}{space 9}2008  {c |}{col 16}{res}{space 2} .0534956{col 28}{space 2} .0189527{col 39}{space 1}    2.82{col 48}{space 3}0.005{col 69}{space 3} .0392721
{txt}{space 9}2012  {c |}{col 16}{res}{space 2}  .085002{col 28}{space 2} .0131571{col 39}{space 1}    6.46{col 48}{space 3}0.000{col 69}{space 3} .1429621
{txt}{space 9}2016  {c |}{col 16}{res}{space 2} .0977853{col 28}{space 2}  .014236{col 39}{space 1}    6.87{col 48}{space 3}0.000{col 69}{space 3} .1450221
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} .0688427{col 28}{space 2} .0203676{col 39}{space 1}    3.38{col 48}{space 3}0.001{col 69}{space 3}        .
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store mod1  
{txt}
{com}.         
. reg ideothermdiff2 valuepol2 sorting2 issextremealt2 interest2 church2 ///
>         income2 edu2 age2 female black hispanic south i.year, beta

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     6,144
{txt}{hline 13}{c +}{hline 34}   F(19, 6124)     = {res}   171.15
{txt}       Model {c |} {res} 154.056809        19  8.10825311   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 290.121104     6,124  .047374445   {txt}R-squared       ={res}    0.3468
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3448
{txt}       Total {c |} {res} 444.177913     6,143  .072306351   {txt}Root MSE        =   {res} .21766

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ideothermdiff2{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 69}        Beta
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}valuepol2 {c |}{col 16}{res}{space 2} .2280329{col 28}{space 2} .0142032{col 39}{space 1}   16.06{col 48}{space 3}0.000{col 69}{space 3}  .182833
{txt}{space 6}sorting2 {c |}{col 16}{res}{space 2} .4099336{col 28}{space 2} .0124166{col 39}{space 1}   33.02{col 48}{space 3}0.000{col 69}{space 3} .3765597
{txt}issextremealt2 {c |}{col 16}{res}{space 2} .1241708{col 28}{space 2} .0122957{col 39}{space 1}   10.10{col 48}{space 3}0.000{col 69}{space 3} .1084878
{txt}{space 5}interest2 {c |}{col 16}{res}{space 2} .0698073{col 28}{space 2}  .014543{col 39}{space 1}    4.80{col 48}{space 3}0.000{col 69}{space 3} .0629859
{txt}{space 7}church2 {c |}{col 16}{res}{space 2} .0164901{col 28}{space 2} .0076668{col 39}{space 1}    2.15{col 48}{space 3}0.032{col 69}{space 3} .0236772
{txt}{space 7}income2 {c |}{col 16}{res}{space 2} .0225234{col 28}{space 2} .0110158{col 39}{space 1}    2.04{col 48}{space 3}0.041{col 69}{space 3} .0238503
{txt}{space 10}edu2 {c |}{col 16}{res}{space 2} .0581537{col 28}{space 2} .0122754{col 39}{space 1}    4.74{col 48}{space 3}0.000{col 69}{space 3} .0553343
{txt}{space 10}age2 {c |}{col 16}{res}{space 2} .0559456{col 28}{space 2} .0143208{col 39}{space 1}    3.91{col 48}{space 3}0.000{col 69}{space 3} .0426904
{txt}{space 8}female {c |}{col 16}{res}{space 2}-.0184717{col 28}{space 2} .0056443{col 39}{space 1}   -3.27{col 48}{space 3}0.001{col 69}{space 3}-.0342568
{txt}{space 9}black {c |}{col 16}{res}{space 2}-.0723331{col 28}{space 2} .0082343{col 39}{space 1}   -8.78{col 48}{space 3}0.000{col 69}{space 3}-.0983376
{txt}{space 6}hispanic {c |}{col 16}{res}{space 2}  -.02844{col 28}{space 2} .0090028{col 39}{space 1}   -3.16{col 48}{space 3}0.002{col 69}{space 3}-.0346268
{txt}{space 9}south {c |}{col 16}{res}{space 2} .0158239{col 28}{space 2} .0059845{col 39}{space 1}    2.64{col 48}{space 3}0.008{col 69}{space 3}  .028039
{txt}{space 14} {c |}
{space 10}year {c |}
{space 9}1992  {c |}{col 16}{res}{space 2}-.0324252{col 28}{space 2} .0133536{col 39}{space 1}   -2.43{col 48}{space 3}0.015{col 69}{space 3}-.0379075
{txt}{space 9}1996  {c |}{col 16}{res}{space 2}-.0163026{col 28}{space 2} .0139715{col 39}{space 1}   -1.17{col 48}{space 3}0.243{col 69}{space 3}-.0173242
{txt}{space 9}2000  {c |}{col 16}{res}{space 2} .0059141{col 28}{space 2} .0233863{col 39}{space 1}    0.25{col 48}{space 3}0.800{col 69}{space 3} .0029036
{txt}{space 9}2004  {c |}{col 16}{res}{space 2}-.0312441{col 28}{space 2} .0161427{col 39}{space 1}   -1.94{col 48}{space 3}0.053{col 69}{space 3}-.0257432
{txt}{space 9}2008  {c |}{col 16}{res}{space 2}-.0394771{col 28}{space 2}  .016796{col 39}{space 1}   -2.35{col 48}{space 3}0.019{col 69}{space 3}-.0308283
{txt}{space 9}2012  {c |}{col 16}{res}{space 2} .0274693{col 28}{space 2} .0117172{col 39}{space 1}    2.34{col 48}{space 3}0.019{col 69}{space 3} .0493503
{txt}{space 9}2016  {c |}{col 16}{res}{space 2} .0946526{col 28}{space 2} .0126531{col 39}{space 1}    7.48{col 48}{space 3}0.000{col 69}{space 3} .1505006
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-.0804309{col 28}{space 2} .0180211{col 39}{space 1}   -4.46{col 48}{space 3}0.000{col 69}{space 3}        .
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store mod2  
{txt}
{com}.         
. reg diffcandtherm2 valuepol2 sorting2 issextremealt2 interest2 church2 ///
>         income2 edu2 age2 female black hispanic south i.year, beta

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     6,239
{txt}{hline 13}{c +}{hline 34}   F(19, 6219)     = {res}   115.96
{txt}       Model {c |} {res} 144.084684        19  7.58340443   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 406.707038     6,219  .065397498   {txt}R-squared       ={res}    0.2616
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2593
{txt}       Total {c |} {res} 550.791723     6,238  .088296204   {txt}Root MSE        =   {res} .25573

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}diffcandtherm2{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 69}        Beta
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}valuepol2 {c |}{col 16}{res}{space 2} .1930376{col 28}{space 2} .0165763{col 39}{space 1}   11.65{col 48}{space 3}0.000{col 69}{space 3} .1396742
{txt}{space 6}sorting2 {c |}{col 16}{res}{space 2} .2370979{col 28}{space 2} .0144324{col 39}{space 1}   16.43{col 48}{space 3}0.000{col 69}{space 3} .1971891
{txt}issextremealt2 {c |}{col 16}{res}{space 2} .1207157{col 28}{space 2} .0142965{col 39}{space 1}    8.44{col 48}{space 3}0.000{col 69}{space 3} .0957236
{txt}{space 5}interest2 {c |}{col 16}{res}{space 2} .2146411{col 28}{space 2} .0168985{col 39}{space 1}   12.70{col 48}{space 3}0.000{col 69}{space 3} .1752531
{txt}{space 7}church2 {c |}{col 16}{res}{space 2}-.0388439{col 28}{space 2} .0089488{col 39}{space 1}   -4.34{col 48}{space 3}0.000{col 69}{space 3}-.0504302
{txt}{space 7}income2 {c |}{col 16}{res}{space 2}-.0122203{col 28}{space 2} .0128641{col 39}{space 1}   -0.95{col 48}{space 3}0.342{col 69}{space 3}-.0117495
{txt}{space 10}edu2 {c |}{col 16}{res}{space 2}-.0361894{col 28}{space 2} .0142963{col 39}{space 1}   -2.53{col 48}{space 3}0.011{col 69}{space 3} -.031319
{txt}{space 10}age2 {c |}{col 16}{res}{space 2} .0763702{col 28}{space 2} .0166178{col 39}{space 1}    4.60{col 48}{space 3}0.000{col 69}{space 3} .0529704
{txt}{space 8}female {c |}{col 16}{res}{space 2} .0411343{col 28}{space 2} .0065863{col 39}{space 1}    6.25{col 48}{space 3}0.000{col 69}{space 3} .0690064
{txt}{space 9}black {c |}{col 16}{res}{space 2} .0924626{col 28}{space 2}  .009558{col 39}{space 1}    9.67{col 48}{space 3}0.000{col 69}{space 3} .1145807
{txt}{space 6}hispanic {c |}{col 16}{res}{space 2} .0418105{col 28}{space 2} .0104969{col 39}{space 1}    3.98{col 48}{space 3}0.000{col 69}{space 3} .0460247
{txt}{space 9}south {c |}{col 16}{res}{space 2}-.0101574{col 28}{space 2} .0069887{col 39}{space 1}   -1.45{col 48}{space 3}0.146{col 69}{space 3}-.0162871
{txt}{space 14} {c |}
{space 10}year {c |}
{space 9}1992  {c |}{col 16}{res}{space 2} -.014289{col 28}{space 2} .0153685{col 39}{space 1}   -0.93{col 48}{space 3}0.353{col 69}{space 3}-.0151591
{txt}{space 9}1996  {c |}{col 16}{res}{space 2}   .03069{col 28}{space 2} .0161488{col 39}{space 1}    1.90{col 48}{space 3}0.057{col 69}{space 3} .0294287
{txt}{space 9}2000  {c |}{col 16}{res}{space 2}-.0090004{col 28}{space 2} .0266573{col 39}{space 1}   -0.34{col 48}{space 3}0.736{col 69}{space 3}-.0040919
{txt}{space 9}2004  {c |}{col 16}{res}{space 2} .0996328{col 28}{space 2} .0186567{col 39}{space 1}    5.34{col 48}{space 3}0.000{col 69}{space 3} .0744048
{txt}{space 9}2008  {c |}{col 16}{res}{space 2} .0207989{col 28}{space 2} .0193123{col 39}{space 1}    1.08{col 48}{space 3}0.282{col 69}{space 3} .0148334
{txt}{space 9}2012  {c |}{col 16}{res}{space 2} .1487119{col 28}{space 2}  .013453{col 39}{space 1}   11.05{col 48}{space 3}0.000{col 69}{space 3} .2413759
{txt}{space 9}2016  {c |}{col 16}{res}{space 2} .2412648{col 28}{space 2} .0145617{col 39}{space 1}   16.57{col 48}{space 3}0.000{col 69}{space 3} .3461281
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} .0343908{col 28}{space 2} .0208397{col 39}{space 1}    1.65{col 48}{space 3}0.099{col 69}{space 3}        .
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store mod3          
{txt}
{com}.            
. estout mod1 mod2 mod3, cells(b(star fmt(3)) se(par fmt(3))) ///
>    legend label varlabels(_cons constant) stats(r2 N, fmt(3 0 1))
{res}
{txt}{hline 68}
{txt}                             mod1            mod2            mod3   
{txt}                             b/se            b/se            b/se   
{txt}{hline 68}
{txt}valuepol2           {res}        0.133***        0.228***        0.193***{txt}
                    {res}      (0.016)         (0.014)         (0.017)   {txt}
{txt}sorting2            {res}        0.342***        0.410***        0.237***{txt}
                    {res}      (0.014)         (0.012)         (0.014)   {txt}
{txt}issextremealt2      {res}        0.101***        0.124***        0.121***{txt}
                    {res}      (0.014)         (0.012)         (0.014)   {txt}
{txt}interest2           {res}        0.144***        0.070***        0.215***{txt}
                    {res}      (0.017)         (0.015)         (0.017)   {txt}
{txt}church2             {res}       -0.031***        0.016*         -0.039***{txt}
                    {res}      (0.009)         (0.008)         (0.009)   {txt}
{txt}income2             {res}       -0.031*          0.023*         -0.012   {txt}
                    {res}      (0.013)         (0.011)         (0.013)   {txt}
{txt}edu2                {res}       -0.058***        0.058***       -0.036*  {txt}
                    {res}      (0.014)         (0.012)         (0.014)   {txt}
{txt}age2                {res}        0.077***        0.056***        0.076***{txt}
                    {res}      (0.016)         (0.014)         (0.017)   {txt}
{txt}female              {res}        0.036***       -0.018**         0.041***{txt}
                    {res}      (0.006)         (0.006)         (0.007)   {txt}
{txt}black               {res}        0.128***       -0.072***        0.092***{txt}
                    {res}      (0.009)         (0.008)         (0.010)   {txt}
{txt}hispanic            {res}        0.062***       -0.028**         0.042***{txt}
                    {res}      (0.010)         (0.009)         (0.010)   {txt}
{txt}South Region Dummy  {res}       -0.003           0.016**        -0.010   {txt}
                    {res}      (0.007)         (0.006)         (0.007)   {txt}
{txt}year=1988           {res}        0.000           0.000           0.000   {txt}
                    {res}          (.)             (.)             (.)   {txt}
{txt}year=1992           {res}       -0.034*         -0.032*         -0.014   {txt}
                    {res}      (0.015)         (0.013)         (0.015)   {txt}
{txt}year=1996           {res}        0.012          -0.016           0.031   {txt}
                    {res}      (0.016)         (0.014)         (0.016)   {txt}
{txt}year=2000           {res}        0.061*          0.006          -0.009   {txt}
                    {res}      (0.026)         (0.023)         (0.027)   {txt}
{txt}year=2004           {res}        0.033          -0.031           0.100***{txt}
                    {res}      (0.018)         (0.016)         (0.019)   {txt}
{txt}year=2008           {res}        0.053**        -0.039*          0.021   {txt}
                    {res}      (0.019)         (0.017)         (0.019)   {txt}
{txt}year=2012           {res}        0.085***        0.027*          0.149***{txt}
                    {res}      (0.013)         (0.012)         (0.013)   {txt}
{txt}year=2016           {res}        0.098***        0.095***        0.241***{txt}
                    {res}      (0.014)         (0.013)         (0.015)   {txt}
{txt}constant            {res}        0.069***       -0.080***        0.034   {txt}
                    {res}      (0.020)         (0.018)         (0.021)   {txt}
{txt}{hline 68}
{txt}r2                  {res}        0.245           0.347           0.262   {txt}
{txt}N                   {res}         6231            6144            6239   {txt}
{txt}{hline 68}
{txt}* p<0.05, ** p<0.01, *** p<0.001

{com}.    
. ****
. ** Estimates used to produce Figure 1
. ** (See "Figures.r" for code)
. ****
. 
. reg partythermdiff2 c.valuepol2##c.yearalt1 sorting2 issextremealt2 ///
>         interest2 church2 income2 edu2 age2 female black hispanic south, beta 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     6,231
{txt}{hline 13}{c +}{hline 34}   F(14, 6216)     = {res}   143.55
{txt}       Model {c |} {res} 125.347724        14  8.95340888   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 387.712465     6,216  .062373305   {txt}R-squared       ={res}    0.2443
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2426
{txt}       Total {c |} {res} 513.060189     6,230   .08235316   {txt}Root MSE        =   {res} .24975

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}partythermdi~2{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 69}        Beta
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}valuepol2 {c |}{col 16}{res}{space 2} .0402022{col 28}{space 2} .0348034{col 39}{space 1}    1.16{col 48}{space 3}0.248{col 69}{space 3}  .030126
{txt}{space 6}yearalt1 {c |}{col 16}{res}{space 2} .0126936{col 28}{space 2} .0023295{col 39}{space 1}    5.45{col 48}{space 3}0.000{col 69}{space 3} .1064946
{txt}{space 14} {c |}
{space 3}c.valuepol2#{c |}
{space 4}c.yearalt1 {c |}{col 16}{res}{space 2}   .01859{col 28}{space 2} .0063716{col 39}{space 1}    2.92{col 48}{space 3}0.004{col 69}{space 3}  .092094
{txt}{space 14} {c |}
{space 6}sorting2 {c |}{col 16}{res}{space 2} .3406087{col 28}{space 2} .0140903{col 39}{space 1}   24.17{col 48}{space 3}0.000{col 69}{space 3} .2933353
{txt}issextremealt2 {c |}{col 16}{res}{space 2} .0999948{col 28}{space 2} .0139182{col 39}{space 1}    7.18{col 48}{space 3}0.000{col 69}{space 3} .0820147
{txt}{space 5}interest2 {c |}{col 16}{res}{space 2} .1446913{col 28}{space 2} .0139928{col 39}{space 1}   10.34{col 48}{space 3}0.000{col 69}{space 3} .1219691
{txt}{space 7}church2 {c |}{col 16}{res}{space 2}-.0318766{col 28}{space 2} .0085054{col 39}{space 1}   -3.75{col 48}{space 3}0.000{col 69}{space 3}-.0428459
{txt}{space 7}income2 {c |}{col 16}{res}{space 2}-.0348167{col 28}{space 2} .0123215{col 39}{space 1}   -2.83{col 48}{space 3}0.005{col 69}{space 3} -.034736
{txt}{space 10}edu2 {c |}{col 16}{res}{space 2}-.0544186{col 28}{space 2} .0138774{col 39}{space 1}   -3.92{col 48}{space 3}0.000{col 69}{space 3}-.0488261
{txt}{space 10}age2 {c |}{col 16}{res}{space 2} .0769008{col 28}{space 2} .0162047{col 39}{space 1}    4.75{col 48}{space 3}0.000{col 69}{space 3} .0552579
{txt}{space 8}female {c |}{col 16}{res}{space 2} .0369454{col 28}{space 2} .0064343{col 39}{space 1}    5.74{col 48}{space 3}0.000{col 69}{space 3} .0641744
{txt}{space 9}black {c |}{col 16}{res}{space 2} .1296517{col 28}{space 2}  .009251{col 39}{space 1}   14.01{col 48}{space 3}0.000{col 69}{space 3} .1667802
{txt}{space 6}hispanic {c |}{col 16}{res}{space 2} .0638821{col 28}{space 2} .0101771{col 39}{space 1}    6.28{col 48}{space 3}0.000{col 69}{space 3} .0727719
{txt}{space 9}south {c |}{col 16}{res}{space 2} -.002302{col 28}{space 2} .0068156{col 39}{space 1}   -0.34{col 48}{space 3}0.736{col 69}{space 3}-.0038257
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .0672705{col 28}{space 2} .0198328{col 39}{space 1}    3.39{col 48}{space 3}0.001{col 69}{space 3}        .
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store mod4          
{txt}
{com}. margins, dydx(valuepol2) at(c.yearalt1=(0(1)7))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     6,231
{txt}Model VCE{col 14}: {res}OLS

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:valuepol2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}7}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}valuepol2    {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0402022{col 26}{space 2} .0348034{col 37}{space 1}    1.16{col 46}{space 3}0.248{col 54}{space 4}-.0280245{col 67}{space 3} .1084289
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0587922{col 26}{space 2} .0293083{col 37}{space 1}    2.01{col 46}{space 3}0.045{col 54}{space 4} .0013377{col 67}{space 3} .1162467
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0773822{col 26}{space 2} .0242461{col 37}{space 1}    3.19{col 46}{space 3}0.001{col 54}{space 4} .0298514{col 67}{space 3}  .124913
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0959722{col 26}{space 2} .0199491{col 37}{space 1}    4.81{col 46}{space 3}0.000{col 54}{space 4} .0568651{col 67}{space 3} .1350794
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1145622{col 26}{space 2} .0170075{col 37}{space 1}    6.74{col 46}{space 3}0.000{col 54}{space 4} .0812217{col 67}{space 3} .1479028
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1331523{col 26}{space 2} .0161783{col 37}{space 1}    8.23{col 46}{space 3}0.000{col 54}{space 4} .1014372{col 67}{space 3} .1648673
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .1517423{col 26}{space 2}   .01776{col 37}{space 1}    8.54{col 46}{space 3}0.000{col 54}{space 4} .1169266{col 67}{space 3} .1865579
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1703323{col 26}{space 2} .0212201{col 37}{space 1}    8.03{col 46}{space 3}0.000{col 54}{space 4} .1287336{col 67}{space 3} .2119309
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg ideothermdiff2 c.valuepol2##c.yearalt1 sorting2 issextremealt2 ///
>         interest2 church2 income2 edu2 age2 female black hispanic south, beta

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     6,144
{txt}{hline 13}{c +}{hline 34}   F(14, 6129)     = {res}   224.97
{txt}       Model {c |} {res} 150.773735        14  10.7695525   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 293.404178     6,129   .04787146   {txt}R-squared       ={res}    0.3394
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3379
{txt}       Total {c |} {res} 444.177913     6,143  .072306351   {txt}Root MSE        =   {res}  .2188

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}ideothermdiff2{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 69}        Beta
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}valuepol2 {c |}{col 16}{res}{space 2}   .12879{col 28}{space 2} .0307694{col 39}{space 1}    4.19{col 48}{space 3}0.000{col 69}{space 3} .1032616
{txt}{space 6}yearalt1 {c |}{col 16}{res}{space 2} .0072618{col 28}{space 2} .0020636{col 39}{space 1}    3.52{col 48}{space 3}0.000{col 69}{space 3} .0647187
{txt}{space 14} {c |}
{space 3}c.valuepol2#{c |}
{space 4}c.yearalt1 {c |}{col 16}{res}{space 2} .0214136{col 28}{space 2} .0056232{col 39}{space 1}    3.81{col 48}{space 3}0.000{col 69}{space 3} .1133711
{txt}{space 14} {c |}
{space 6}sorting2 {c |}{col 16}{res}{space 2} .4129358{col 28}{space 2} .0124647{col 39}{space 1}   33.13{col 48}{space 3}0.000{col 69}{space 3} .3793174
{txt}issextremealt2 {c |}{col 16}{res}{space 2} .1270581{col 28}{space 2} .0122989{col 39}{space 1}   10.33{col 48}{space 3}0.000{col 69}{space 3} .1110104
{txt}{space 5}interest2 {c |}{col 16}{res}{space 2} .0197603{col 28}{space 2} .0123427{col 39}{space 1}    1.60{col 48}{space 3}0.109{col 69}{space 3} .0178293
{txt}{space 7}church2 {c |}{col 16}{res}{space 2} .0281399{col 28}{space 2} .0074937{col 39}{space 1}    3.76{col 48}{space 3}0.000{col 69}{space 3} .0404045
{txt}{space 7}income2 {c |}{col 16}{res}{space 2} .0342487{col 28}{space 2}  .010872{col 39}{space 1}    3.15{col 48}{space 3}0.002{col 69}{space 3} .0362663
{txt}{space 10}edu2 {c |}{col 16}{res}{space 2}  .050828{col 28}{space 2} .0122609{col 39}{space 1}    4.15{col 48}{space 3}0.000{col 69}{space 3} .0483638
{txt}{space 10}age2 {c |}{col 16}{res}{space 2} .0596884{col 28}{space 2} .0143737{col 39}{space 1}    4.15{col 48}{space 3}0.000{col 69}{space 3} .0455464
{txt}{space 8}female {c |}{col 16}{res}{space 2}-.0186479{col 28}{space 2}  .005672{col 39}{space 1}   -3.29{col 48}{space 3}0.001{col 69}{space 3}-.0345835
{txt}{space 9}black {c |}{col 16}{res}{space 2}-.0768875{col 28}{space 2} .0082127{col 39}{space 1}   -9.36{col 48}{space 3}0.000{col 69}{space 3}-.1045292
{txt}{space 6}hispanic {c |}{col 16}{res}{space 2}-.0350384{col 28}{space 2} .0089687{col 39}{space 1}   -3.91{col 48}{space 3}0.000{col 69}{space 3}-.0426606
{txt}{space 9}south {c |}{col 16}{res}{space 2} .0144532{col 28}{space 2} .0060093{col 39}{space 1}    2.41{col 48}{space 3}0.016{col 69}{space 3} .0256103
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.0635394{col 28}{space 2} .0175598{col 39}{space 1}   -3.62{col 48}{space 3}0.000{col 69}{space 3}        .
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store mod5          
{txt}
{com}. margins, dydx(valuepol2) at(c.yearalt1=(0(1)7))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     6,144
{txt}Model VCE{col 14}: {res}OLS

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:valuepol2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}7}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}valuepol2    {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}   .12879{col 26}{space 2} .0307694{col 37}{space 1}    4.19{col 46}{space 3}0.000{col 54}{space 4} .0684711{col 67}{space 3} .1891089
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1502036{col 26}{space 2} .0259147{col 37}{space 1}    5.80{col 46}{space 3}0.000{col 54}{space 4} .0994016{col 67}{space 3} .2010056
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1716171{col 26}{space 2}  .021439{col 37}{space 1}    8.00{col 46}{space 3}0.000{col 54}{space 4} .1295892{col 67}{space 3}  .213645
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1930307{col 26}{space 2} .0176331{col 37}{space 1}   10.95{col 46}{space 3}0.000{col 54}{space 4} .1584637{col 67}{space 3} .2275977
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2144443{col 26}{space 2} .0150153{col 37}{space 1}   14.28{col 46}{space 3}0.000{col 54}{space 4} .1850089{col 67}{space 3} .2438796
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2358578{col 26}{space 2} .0142561{col 37}{space 1}   16.54{col 46}{space 3}0.000{col 54}{space 4} .2079109{col 67}{space 3} .2638048
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .2572714{col 26}{space 2} .0156286{col 37}{space 1}   16.46{col 46}{space 3}0.000{col 54}{space 4} .2266339{col 67}{space 3} .2879089
{txt}{space 10}8  {c |}{col 14}{res}{space 2}  .278685{col 26}{space 2} .0186684{col 37}{space 1}   14.93{col 46}{space 3}0.000{col 54}{space 4} .2420883{col 67}{space 3} .3152816
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         
. reg diffcandtherm2 c.valuepol2##c.yearalt1 sorting2 issextremealt2 ///
>         interest2 church2 income2 edu2 age2 female black hispanic south, beta 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     6,239
{txt}{hline 13}{c +}{hline 34}   F(14, 6224)     = {res}   149.10
{txt}       Model {c |} {res}  138.33015        14  9.88072499   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 412.461573     6,224  .066269533   {txt}R-squared       ={res}    0.2511
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2495
{txt}       Total {c |} {res} 550.791723     6,238  .088296204   {txt}Root MSE        =   {res} .25743

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}diffcandtherm2{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      t{col 48}   P>|t|{col 69}        Beta
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}valuepol2 {c |}{col 16}{res}{space 2} .1001163{col 28}{space 2} .0358301{col 39}{space 1}    2.79{col 48}{space 3}0.005{col 69}{space 3} .0724401
{txt}{space 6}yearalt1 {c |}{col 16}{res}{space 2}  .027874{col 28}{space 2} .0023999{col 39}{space 1}   11.61{col 48}{space 3}0.000{col 69}{space 3} .2258932
{txt}{space 14} {c |}
{space 3}c.valuepol2#{c |}
{space 4}c.yearalt1 {c |}{col 16}{res}{space 2} .0207947{col 28}{space 2} .0065588{col 39}{space 1}    3.17{col 48}{space 3}0.002{col 69}{space 3} .0994896
{txt}{space 14} {c |}
{space 6}sorting2 {c |}{col 16}{res}{space 2} .2401806{col 28}{space 2} .0145078{col 39}{space 1}   16.56{col 48}{space 3}0.000{col 69}{space 3} .1997529
{txt}issextremealt2 {c |}{col 16}{res}{space 2} .1213602{col 28}{space 2} .0143185{col 39}{space 1}    8.48{col 48}{space 3}0.000{col 69}{space 3} .0962346
{txt}{space 5}interest2 {c |}{col 16}{res}{space 2} .1608438{col 28}{space 2}  .014392{col 39}{space 1}   11.18{col 48}{space 3}0.000{col 69}{space 3}  .131328
{txt}{space 7}church2 {c |}{col 16}{res}{space 2}-.0260856{col 28}{space 2} .0087605{col 39}{space 1}   -2.98{col 48}{space 3}0.003{col 69}{space 3}-.0338664
{txt}{space 7}income2 {c |}{col 16}{res}{space 2} .0013657{col 28}{space 2} .0127067{col 39}{space 1}    0.11{col 48}{space 3}0.914{col 69}{space 3} .0013131
{txt}{space 10}edu2 {c |}{col 16}{res}{space 2}-.0443608{col 28}{space 2} .0143006{col 39}{space 1}   -3.10{col 48}{space 3}0.002{col 69}{space 3}-.0383907
{txt}{space 10}age2 {c |}{col 16}{res}{space 2}  .080438{col 28}{space 2} .0166982{col 39}{space 1}    4.82{col 48}{space 3}0.000{col 69}{space 3} .0557919
{txt}{space 8}female {c |}{col 16}{res}{space 2} .0403669{col 28}{space 2} .0066271{col 39}{space 1}    6.09{col 48}{space 3}0.000{col 69}{space 3} .0677189
{txt}{space 9}black {c |}{col 16}{res}{space 2} .0866352{col 28}{space 2} .0095462{col 39}{space 1}    9.08{col 48}{space 3}0.000{col 69}{space 3} .1073593
{txt}{space 6}hispanic {c |}{col 16}{res}{space 2} .0321828{col 28}{space 2} .0104743{col 39}{space 1}    3.07{col 48}{space 3}0.002{col 69}{space 3} .0354266
{txt}{space 9}south {c |}{col 16}{res}{space 2}-.0124893{col 28}{space 2} .0070274{col 39}{space 1}   -1.78{col 48}{space 3}0.076{col 69}{space 3}-.0200262
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .0530884{col 28}{space 2} .0204253{col 39}{space 1}    2.60{col 48}{space 3}0.009{col 69}{space 3}        .
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store mod6          
{txt}
{com}. margins, dydx(valuepol2) at(c.yearalt1=(0(1)7))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     6,239
{txt}Model VCE{col 14}: {res}OLS

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:valuepol2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:yearalt1}{space 8}{txt:=} {space 10}7}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}valuepol2    {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1001163{col 26}{space 2} .0358301{col 37}{space 1}    2.79{col 46}{space 3}0.005{col 54}{space 4}  .029877{col 67}{space 3} .1703555
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1209109{col 26}{space 2} .0301744{col 37}{space 1}    4.01{col 46}{space 3}0.000{col 54}{space 4} .0617587{col 67}{space 3} .1800631
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1417056{col 26}{space 2} .0249646{col 37}{space 1}    5.68{col 46}{space 3}0.000{col 54}{space 4} .0927664{col 67}{space 3} .1906448
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1625002{col 26}{space 2} .0205427{col 37}{space 1}    7.91{col 46}{space 3}0.000{col 54}{space 4} .1222294{col 67}{space 3} .2027711
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1832949{col 26}{space 2} .0175161{col 37}{space 1}   10.46{col 46}{space 3}0.000{col 54}{space 4} .1489572{col 67}{space 3} .2176325
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2040895{col 26}{space 2} .0166632{col 37}{space 1}   12.25{col 46}{space 3}0.000{col 54}{space 4} .1714239{col 67}{space 3} .2367552
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .2248842{col 26}{space 2} .0182906{col 37}{space 1}   12.30{col 46}{space 3}0.000{col 54}{space 4} .1890282{col 67}{space 3} .2607401
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2456788{col 26}{space 2} .0218511{col 37}{space 1}   11.24{col 46}{space 3}0.000{col 54}{space 4} .2028432{col 67}{space 3} .2885144
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         
. estout mod4 mod5 mod6, cells(b(star fmt(3)) se(par fmt(3))) ///
>    legend label varlabels(_cons constant) stats(r2 N, fmt(3 0 1))
{res}
{txt}{hline 68}
{txt}                             mod4            mod5            mod6   
{txt}                             b/se            b/se            b/se   
{txt}{hline 68}
{txt}valuepol2           {res}        0.040           0.129***        0.100** {txt}
                    {res}      (0.035)         (0.031)         (0.036)   {txt}
{txt}yearalt1            {res}        0.013***        0.007***        0.028***{txt}
                    {res}      (0.002)         (0.002)         (0.002)   {txt}
{txt}valuepol2 # yearalt1{res}        0.019**         0.021***        0.021** {txt}
                    {res}      (0.006)         (0.006)         (0.007)   {txt}
{txt}sorting2            {res}        0.341***        0.413***        0.240***{txt}
                    {res}      (0.014)         (0.012)         (0.015)   {txt}
{txt}issextremealt2      {res}        0.100***        0.127***        0.121***{txt}
                    {res}      (0.014)         (0.012)         (0.014)   {txt}
{txt}interest2           {res}        0.145***        0.020           0.161***{txt}
                    {res}      (0.014)         (0.012)         (0.014)   {txt}
{txt}church2             {res}       -0.032***        0.028***       -0.026** {txt}
                    {res}      (0.009)         (0.007)         (0.009)   {txt}
{txt}income2             {res}       -0.035**         0.034**         0.001   {txt}
                    {res}      (0.012)         (0.011)         (0.013)   {txt}
{txt}edu2                {res}       -0.054***        0.051***       -0.044** {txt}
                    {res}      (0.014)         (0.012)         (0.014)   {txt}
{txt}age2                {res}        0.077***        0.060***        0.080***{txt}
                    {res}      (0.016)         (0.014)         (0.017)   {txt}
{txt}female              {res}        0.037***       -0.019**         0.040***{txt}
                    {res}      (0.006)         (0.006)         (0.007)   {txt}
{txt}black               {res}        0.130***       -0.077***        0.087***{txt}
                    {res}      (0.009)         (0.008)         (0.010)   {txt}
{txt}hispanic            {res}        0.064***       -0.035***        0.032** {txt}
                    {res}      (0.010)         (0.009)         (0.010)   {txt}
{txt}South Region Dummy  {res}       -0.002           0.014*         -0.012   {txt}
                    {res}      (0.007)         (0.006)         (0.007)   {txt}
{txt}constant            {res}        0.067***       -0.064***        0.053** {txt}
                    {res}      (0.020)         (0.018)         (0.020)   {txt}
{txt}{hline 68}
{txt}r2                  {res}        0.244           0.339           0.251   {txt}
{txt}N                   {res}         6231            6144            6239   {txt}
{txt}{hline 68}
{txt}* p<0.05, ** p<0.01, *** p<0.001

{com}.         
. 
. *saveold "values-polarization2.dta", replace version(12)                
. 
{txt}end of do-file

{com}. clear

. use "/Users/adamenders/Dropbox/Value Polarization and Affective Polarization/Code and Data/For Dataverse/anes_mergedfile_1992to1997.dta"

. do "/var/folders/xb/ddtsf7g93xd57f7hhtnm9lyc0000gp/T//SD07120.000000"
{txt}
{com}. * Case ID
. gen id92 = VID92 
{txt}(1,434 missing values generated)

{com}. gen id94 = VID94 
{txt}(644 missing values generated)

{com}. gen id96 = VID96
{txt}(725 missing values generated)

{com}. 
. keep if id92 != . & id96 != .
{txt}(1,842 observations deleted)

{com}. 
. 
. * Weight
. gen weight92 = V923009
{txt}
{com}. gen weight94 = V940005 
{txt}
{com}. gen weight96 = V960004
{txt}
{com}. 
. 
. * Party identification
. gen pid92 = V923634 - 3
{txt}
{com}. replace pid92 = . if pid92 > 3
{txt}(8 real changes made, 8 to missing)

{com}. gen pid94 = V940655 - 3
{txt}
{com}. replace pid94 = . if pid94 > 3
{txt}(3 real changes made, 3 to missing)

{com}. gen pid96 = V960420 - 3
{txt}
{com}. replace pid96 = . if pid96 > 3
{txt}(6 real changes made, 6 to missing)

{com}. 
. gen rep92 = 1 if pid92 > 1
{txt}(430 missing values generated)

{com}. replace rep92 = 0 if pid92 < -1
{txt}(191 real changes made)

{com}. gen rep94 = 1 if pid94 > 1
{txt}(405 missing values generated)

{com}. replace rep94 = 0 if pid94 < -1
{txt}(204 real changes made)

{com}. gen rep96 = 1 if pid96 > 1
{txt}(416 missing values generated)

{com}. replace rep96 = 0 if pid96 < -1
{txt}(223 real changes made)

{com}. 
. 
. * Party ID strength
. gen pidstrength92 = 3 if abs(pid92) == 3
{txt}(432 missing values generated)

{com}. replace pidstrength92 = 2 if abs(pid92) == 2
{txt}(185 real changes made)

{com}. replace pidstrength92 = 1 if abs(pid92) == 1
{txt}(176 real changes made)

{com}. replace pidstrength92 = 0 if abs(pid92) == 0
{txt}(63 real changes made)

{com}. 
. gen pidstrength94 = 3 if abs(pid94) == 3
{txt}(396 missing values generated)

{com}. replace pidstrength94 = 2 if abs(pid94) == 2
{txt}(192 real changes made)

{com}. replace pidstrength94 = 1 if abs(pid94) == 1
{txt}(154 real changes made)

{com}. replace pidstrength94 = 0 if abs(pid94) == 0
{txt}(47 real changes made)

{com}. 
. gen pidstrength96 = 3 if abs(pid96) == 3
{txt}(407 missing values generated)

{com}. replace pidstrength96 = 2 if abs(pid96) == 2
{txt}(208 real changes made)

{com}. replace pidstrength96 = 1 if abs(pid96) == 1
{txt}(151 real changes made)

{com}. replace pidstrength96 = 0 if abs(pid96) == 0
{txt}(42 real changes made)

{com}. 
. 
. * Ideology
. gen ideo92 = V923509 - 4
{txt}
{com}. replace ideo92 = . if abs(ideo92) > 3 
{txt}(122 real changes made, 122 to missing)

{com}. gen ideo94 = V940839 - 4
{txt}
{com}. replace ideo94 = . if abs(ideo94) > 3 
{txt}(95 real changes made, 95 to missing)

{com}. gen ideo96 = V960365 - 4
{txt}
{com}. replace ideo96 = . if abs(ideo96) > 3 
{txt}(101 real changes made, 101 to missing)

{com}. 
. gen conserv92 = 1 if ideo92 > 0
{txt}(269 missing values generated)

{com}. replace conserv92 = 0 if ideo92 < 0
{txt}(132 real changes made)

{com}. gen conserv94 = 1 if ideo94 > 0
{txt}(268 missing values generated)

{com}. replace conserv94 = 0 if ideo94 < 0
{txt}(123 real changes made)

{com}. gen conserv96 = 1 if ideo96 > 0
{txt}(261 missing values generated)

{com}. replace conserv96 = 0 if ideo96 < 0
{txt}(132 real changes made)

{com}. 
. 
. * Ideological strength
. gen ideostrength92 = 3 if abs(ideo92) == 3
{txt}(561 missing values generated)

{com}. replace ideostrength92 = 2 if abs(ideo92) == 2
{txt}(145 real changes made)

{com}. replace ideostrength92 = 1 if abs(ideo92) == 1
{txt}(157 real changes made)

{com}. replace ideostrength92 = 0 if abs(ideo92) == 0
{txt}(137 real changes made)

{com}. 
. gen ideostrength94 = 3 if abs(ideo94) == 3
{txt}(570 missing values generated)

{com}. replace ideostrength94 = 2 if abs(ideo94) == 2
{txt}(166 real changes made)

{com}. replace ideostrength94 = 1 if abs(ideo94) == 1
{txt}(164 real changes made)

{com}. replace ideostrength94 = 0 if abs(ideo94) == 0
{txt}(145 real changes made)

{com}. 
. gen ideostrength96 = 3 if abs(ideo96) == 3
{txt}(573 missing values generated)

{com}. replace ideostrength96 = 2 if abs(ideo96) == 2
{txt}(158 real changes made)

{com}. replace ideostrength96 = 1 if abs(ideo96) == 1
{txt}(185 real changes made)

{com}. replace ideostrength96 = 0 if abs(ideo96) == 0
{txt}(129 real changes made)

{com}. 
. 
. * Sorting
. replace ideostrength92 = ideostrength92 + 1
{txt}(475 real changes made)

{com}. replace pidstrength92 = pidstrength92 + 1
{txt}(589 real changes made)

{com}. gen sorting92 = abs(pid92 - (-1 * ideo92)) * ideostrength92 * pidstrength92
{txt}(124 missing values generated)

{com}. 
. replace ideostrength94 = ideostrength94 + 1
{txt}(502 real changes made)

{com}. replace pidstrength94 = pidstrength94 + 1
{txt}(594 real changes made)

{com}. gen sorting94 = abs(pid94 - (-1 * ideo94)) * ideostrength94 * pidstrength94
{txt}(95 missing values generated)

{com}. 
. replace ideostrength96 = ideostrength96 + 1
{txt}(496 real changes made)

{com}. replace pidstrength96 = pidstrength96 + 1
{txt}(591 real changes made)

{com}. gen sorting96 = abs(pid96 - (-1 * ideo96)) * ideostrength96 * pidstrength96
{txt}(104 missing values generated)

{com}. 
. 
. * Education (ranges from 1-7)
. gen edu92 = V923908
{txt}
{com}. replace edu92 = . if edu92 >= 8
{txt}(20 real changes made, 20 to missing)

{com}. replace edu92 = . if edu92 < 1
{txt}(0 real changes made)

{com}. label define edulab 1 "8 grades or less" 2 "9-12 grades" 3 "High school" ///
>         4 "HS + non-academic training" 5 "Some college" 6 "BA" 7 "Advanced"
{txt}
{com}. label values edu edulab
{txt}
{com}. 
. 
. * Family income (1-24)
. gen income92 = V924104
{txt}
{com}. replace income92 = . if income > 24
{txt}(45 real changes made, 45 to missing)

{com}. 
. 
. * Race 
. gen race92 = V924202
{txt}
{com}. replace race92 = . if race92 == 9
{txt}(1 real change made, 1 to missing)

{com}. 
. gen white92 = 0
{txt}
{com}. replace white92 = 1 if race92 == 1
{txt}(500 real changes made)

{com}. 
. gen black92 = 0
{txt}
{com}. replace black92 = 1 if race92 == 2
{txt}(78 real changes made)

{com}. 
. 
. * Gender (1=female)
. gen female92 = V924201 - 1
{txt}
{com}. replace female92 = . if female92 < 0
{txt}(0 real changes made)

{com}. replace female92 = . if female92 > 1
{txt}(0 real changes made)

{com}. label define genderlab 0 "Male" 1 "Female"
{txt}
{com}. label values female92 genderlab
{txt}
{com}. 
. 
. * Age (number of years) 
. gen age92 = V923903
{txt}
{com}. replace age92 = . if age92 > 91
{txt}(0 real changes made)

{com}. replace age92 = . if age92 < 17
{txt}(0 real changes made)

{com}. 
. 
. * Region
. gen south92 = .
{txt}(597 missing values generated)

{com}. replace south92 = 0 
{txt}(597 real changes made)

{com}. replace south92 = 1 if V923014 == 3
{txt}(218 real changes made)

{com}. label define southern 0 "0 Non-South" 1 "1 South"
{txt}
{com}. label values south southern
{txt}
{com}. 
. 
. * Church attendance
. gen church92 = V923821
{txt}
{com}. replace church92 = . if church92 < 1
{txt}(137 real changes made, 137 to missing)

{com}. recode church92 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(church92: 458 changes made)

{com}. 
. 
. * Interest in campaigns 
. gen interest92 = V925102
{txt}
{com}. replace interest92 = . if interest92 > 5
{txt}(2 real changes made, 2 to missing)

{com}. replace interest92 = . if interest92 < 1
{txt}(0 real changes made)

{com}. recode interest92 (1=3) (3=2) (5=1)
{txt}(interest92: 595 changes made)

{com}. label define interestlab 1 "Not much interested" ///
>         2 "Somewhat interested" 3 "Very much interested"
{txt}
{com}. label values interest interestlab
{txt}
{com}. 
. 
. * Interviewer information assessment
. gen info92 = V924205
{txt}
{com}. replace info92 = . if info92 > 9
{txt}(0 real changes made)

{com}. recode info92 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(info92: 597 changes made)

{com}. 
. 
. * Party feeling thermometers
. gen reptherm92 = V923318
{txt}
{com}. replace reptherm92 = . if reptherm92 > 100
{txt}(22 real changes made, 22 to missing)

{com}. gen demtherm92 = V923317
{txt}
{com}. replace demtherm92 = . if demtherm92 > 100
{txt}(22 real changes made, 22 to missing)

{com}. gen partydifftherm92 = abs(demtherm92 - reptherm92)
{txt}(24 missing values generated)

{com}. 
. gen reptherm94 = V940302
{txt}
{com}. replace reptherm94 = . if reptherm94 > 100
{txt}(12 real changes made, 12 to missing)

{com}. gen demtherm94 = V940301
{txt}
{com}. replace demtherm94 = . if demtherm94 > 100
{txt}(8 real changes made, 8 to missing)

{com}. gen partydifftherm94 = abs(demtherm94 - reptherm94)
{txt}(12 missing values generated)

{com}. 
. gen reptherm96 = V960293
{txt}
{com}. replace reptherm96 = . if reptherm96 > 100
{txt}(13 real changes made, 13 to missing)

{com}. gen demtherm96 = V960292
{txt}
{com}. replace demtherm96 = . if demtherm96 > 100
{txt}(9 real changes made, 9 to missing)

{com}. gen partydifftherm96 = abs(demtherm96 - reptherm96)
{txt}(13 missing values generated)

{com}. 
. 
. * Candidate feeling thermometers
. gen rcandtherm92 = V923305
{txt}
{com}. replace rcandtherm92 = . if rcandtherm92 > 100
{txt}(7 real changes made, 7 to missing)

{com}. gen dcandtherm92 = V923306
{txt}
{com}. replace dcandtherm92 = . if dcandtherm92 > 100
{txt}(16 real changes made, 16 to missing)

{com}. gen diffcandtherm92 = abs(dcandtherm92 - rcandtherm92)
{txt}(19 missing values generated)

{com}. 
. gen rcandtherm96 = V960273
{txt}
{com}. replace rcandtherm96 = . if rcandtherm96 > 100
{txt}(10 real changes made, 10 to missing)

{com}. gen dcandtherm96 = V960272
{txt}
{com}. replace dcandtherm96 = . if dcandtherm96 > 100
{txt}(2 real changes made, 2 to missing)

{com}. gen diffcandtherm96 = abs(dcandtherm96 - rcandtherm96)
{txt}(11 missing values generated)

{com}. 
. 
. * Ideological group feeling thermometers
. gen contherm92 = V925319
{txt}
{com}. replace contherm92 = . if contherm92 > 100
{txt}(33 real changes made, 33 to missing)

{com}. gen libtherm92 = V925326
{txt}
{com}. replace libtherm92 = . if libtherm92 > 100
{txt}(28 real changes made, 28 to missing)

{com}. gen diffideotherm92 = abs(libtherm92 - contherm92)
{txt}(39 missing values generated)

{com}. 
. gen contherm94 = V940306
{txt}
{com}. replace contherm94 = . if contherm94 > 100
{txt}(28 real changes made, 28 to missing)

{com}. gen libtherm94 = V940311
{txt}
{com}. replace libtherm94 = . if libtherm94 > 100
{txt}(19 real changes made, 19 to missing)

{com}. gen diffideotherm94 = abs(libtherm94 - contherm94)
{txt}(32 missing values generated)

{com}. 
. gen contherm96 = V961031
{txt}
{com}. replace contherm96 = . if contherm96 > 100
{txt}(71 real changes made, 71 to missing)

{com}. gen libtherm96 = V961032
{txt}
{com}. replace libtherm96 = . if libtherm96 > 100
{txt}(71 real changes made, 71 to missing)

{com}. gen diffideotherm96 = abs(libtherm96 - contherm96)
{txt}(74 missing values generated)

{com}. 
. 
. * Affective polarization
. alpha diffideotherm92 diffcandtherm92 partydifftherm92, gen(affectpol92)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 259.8027
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.6884
{txt}
{com}. 
. alpha diffideotherm96 diffcandtherm96 partydifftherm96, gen(affectpol96)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 339.1357
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.7683
{txt}
{com}. 
. 
. * Government spending and services
. gen selfservice92 = V923701
{txt}
{com}. replace selfservice92 = . if selfservice92 < 1
{txt}(76 real changes made, 76 to missing)

{com}. replace selfservice92 = . if selfservice92 >= 8
{txt}(3 real changes made, 3 to missing)

{com}. recode selfservice92 (1=3) (2=2) (3=1) (4=0) (5=-1) (6=-2) (7=-3)
{txt}(selfservice92: 456 changes made)

{com}. label define servicelab -3 "Government should provide many more services" ///
>         3 "Government should provide many ewer services"
{txt}
{com}. label values selfservice92 servicelab
{txt}
{com}. 
. gen repservice92 = V923702
{txt}
{com}. replace repservice92 = . if repservice92 < 1
{txt}(79 real changes made, 79 to missing)

{com}. replace repservice92 = . if repservice92 >= 8
{txt}(30 real changes made, 30 to missing)

{com}. recode repservice92 (1=3) (2=2) (3=1) (4=0) (5=-1) (6=-2) (7=-3)
{txt}(repservice92: 375 changes made)

{com}. 
. gen demservice92 = V923703
{txt}
{com}. replace demservice92 = . if demservice92 < 1
{txt}(79 real changes made, 79 to missing)

{com}. replace demservice92 = . if demservice92 >= 8
{txt}(40 real changes made, 40 to missing)

{com}. recode demservice92 (1=3) (2=2) (3=1) (4=0) (5=-1) (6=-2) (7=-3)
{txt}(demservice92: 456 changes made)

{com}. 
. gen pdiffservice92 = abs(repservice92 - demservice92)
{txt}(125 missing values generated)

{com}. 
. 
. gen selfservice96 = V960450
{txt}
{com}. replace selfservice96 = . if selfservice96 < 1
{txt}(67 real changes made, 67 to missing)

{com}. replace selfservice96 = . if selfservice96 >= 8
{txt}(3 real changes made, 3 to missing)

{com}. recode selfservice96 (1=3) (2=2) (3=1) (4=0) (5=-1) (6=-2) (7=-3)
{txt}(selfservice96: 454 changes made)

{com}. 
. gen repservice96 = V960455
{txt}
{com}. replace repservice96 = . if repservice96 < 1
{txt}(0 real changes made)

{com}. replace repservice96 = . if repservice96 >= 8
{txt}(49 real changes made, 49 to missing)

{com}. recode repservice96 (1=3) (2=2) (3=1) (4=0) (5=-1) (6=-2) (7=-3)
{txt}(repservice96: 404 changes made)

{com}. 
. gen demservice96 = V960453
{txt}
{com}. replace demservice96 = . if demservice96 < 1
{txt}(0 real changes made)

{com}. replace demservice96 = . if demservice96 >= 8
{txt}(24 real changes made, 24 to missing)

{com}. recode demservice96 (1=3) (2=2) (3=1) (4=0) (5=-1) (6=-2) (7=-3)
{txt}(demservice96: 558 changes made)

{com}. 
. gen pdiffservice96 = abs(repservice96 - demservice96)
{txt}(54 missing values generated)

{com}. 
. 
. * Defense spending
. gen selfdefense92 = V923707
{txt}
{com}. replace selfdefense92 = . if selfdefense92 < 1
{txt}(56 real changes made, 56 to missing)

{com}. replace selfdefense92 = . if selfdefense92 >= 8
{txt}(4 real changes made, 4 to missing)

{com}. recode selfdefense92 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfdefense92: 537 changes made)

{com}. label define defenselab -3 "Greatly decrease defense spending" ///
>         3 "Greatly increase defense spending"
{txt}
{com}. label values selfdefense92 defenselab
{txt}
{com}. 
. gen repdefense92 = V923708
{txt}
{com}. replace repdefense92 = . if repdefense92 < 1
{txt}(60 real changes made, 60 to missing)

{com}. replace repdefense92 = . if repdefense92 >= 8
{txt}(19 real changes made, 19 to missing)

{com}. recode repdefense92 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repdefense92: 518 changes made)

{com}. 
. gen demdefense92 = V923709
{txt}
{com}. replace demdefense92 = . if demdefense92 < 1
{txt}(60 real changes made, 60 to missing)

{com}. replace demdefense92 = . if demdefense92 >= 8
{txt}(69 real changes made, 69 to missing)

{com}. recode demdefense92 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demdefense92: 468 changes made)

{com}. 
. gen pdiffdefense92 = abs(repdefense92 - demdefense92)
{txt}(130 missing values generated)

{com}. 
. 
. gen selfdefense96 = V960463
{txt}
{com}. replace selfdefense96 = . if selfdefense96 < 1
{txt}(62 real changes made, 62 to missing)

{com}. replace selfdefense96 = . if selfdefense96 >= 8
{txt}(2 real changes made, 2 to missing)

{com}. recode selfdefense96 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfdefense96: 533 changes made)

{com}. 
. gen repdefense96 = V960469
{txt}
{com}. replace repdefense96 = . if repdefense96 < 1
{txt}(0 real changes made)

{com}. replace repdefense96 = . if repdefense96 >= 8
{txt}(69 real changes made, 69 to missing)

{com}. recode repdefense96 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repdefense96: 528 changes made)

{com}. 
. gen demdefense96 = V960466
{txt}
{com}. replace demdefense96 = . if demdefense96 < 1
{txt}(0 real changes made)

{com}. replace demdefense96 = . if demdefense96 >= 8
{txt}(36 real changes made, 36 to missing)

{com}. recode demdefense96 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demdefense96: 561 changes made)

{com}. 
. gen pdiffdefense96 = abs(repdefense96 - demdefense96)
{txt}(73 missing values generated)

{com}. 
. 
. * Health insurance
. gen selfinsure92 = V923716
{txt}
{com}. replace selfinsure92 = . if selfinsure92 < 1
{txt}(63 real changes made, 63 to missing)

{com}. replace selfinsure92 = . if selfinsure92 > 7
{txt}(12 real changes made, 12 to missing)

{com}. recode selfinsure92 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfinsure92: 522 changes made)

{com}. label define insurelab -3 "Government insurance plan" 3 "Private insurance plan"
{txt}
{com}. label values selfinsure92 insurelab
{txt}
{com}. 
. 
. gen selfinsure96 = V960479
{txt}
{com}. replace selfinsure96 = . if selfinsure96 < 1
{txt}(52 real changes made, 52 to missing)

{com}. replace selfinsure96 = . if selfinsure96 > 7
{txt}(7 real changes made, 7 to missing)

{com}. recode selfinsure96 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfinsure96: 538 changes made)

{com}. 
. gen repinsure96 = V960481
{txt}
{com}. replace repinsure96 = . if repinsure96 < 1
{txt}(0 real changes made)

{com}. replace repinsure96 = . if repinsure96 > 7
{txt}(80 real changes made, 80 to missing)

{com}. recode repinsure96 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repinsure96: 517 changes made)

{com}. 
. gen deminsure96 = V960480
{txt}
{com}. replace deminsure96 = . if deminsure96 < 1
{txt}(0 real changes made)

{com}. replace deminsure96 = . if deminsure96 > 7
{txt}(40 real changes made, 40 to missing)

{com}. recode deminsure96 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(deminsure96: 557 changes made)

{com}. 
. gen pdiffinsure96 = abs(repinsure96 - deminsure96)
{txt}(89 missing values generated)

{com}. 
. 
. * Guarenteed jobs
. gen selfjobs92 = V923718
{txt}
{com}. replace selfjobs92 = . if selfjobs92 < 1
{txt}(50 real changes made, 50 to missing)

{com}. replace selfjobs92 = . if selfjobs92 >= 8
{txt}(5 real changes made, 5 to missing)

{com}. recode selfjobs92 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfjobs92: 542 changes made)

{com}. label define jobslab -3 "Government see to job and good standard of living" ///
>         3 "Government let each person get ahead on his own"
{txt}
{com}. label values selfjobs92 jobslab
{txt}
{com}. 
. gen repjobs92 = V923719
{txt}
{com}. replace repjobs92 = . if repjobs92 < 1
{txt}(2 real changes made, 2 to missing)

{com}. replace repjobs92 = . if repjobs92 >= 8
{txt}(55 real changes made, 55 to missing)

{com}. recode repjobs92 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repjobs92: 540 changes made)

{com}. 
. gen demjobs92 = V923720
{txt}
{com}. replace demjobs92 = . if demjobs92 < 1
{txt}(2 real changes made, 2 to missing)

{com}. replace demjobs92 = . if demjobs92 >= 8
{txt}(83 real changes made, 83 to missing)

{com}. recode demjobs92 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demjobs92: 512 changes made)

{com}. 
. gen pdiffjobs92 = abs(repjobs92 - demjobs92)
{txt}(93 missing values generated)

{com}. 
. 
. gen selfjobs96 = V960483
{txt}
{com}. replace selfjobs96 = . if selfjobs96 < 1
{txt}(44 real changes made, 44 to missing)

{com}. replace selfjobs96 = . if selfjobs96 >= 8
{txt}(3 real changes made, 3 to missing)

{com}. recode selfjobs96 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfjobs96: 550 changes made)

{com}. 
. gen repjobs96 = V960485
{txt}
{com}. replace repjobs96 = . if repjobs96 < 1
{txt}(0 real changes made)

{com}. replace repjobs96 = . if repjobs96 >= 8
{txt}(60 real changes made, 60 to missing)

{com}. recode repjobs96 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repjobs96: 537 changes made)

{com}. 
. gen demjobs96 = V960484
{txt}
{com}. replace demjobs96 = . if demjobs96 < 1
{txt}(0 real changes made)

{com}. replace demjobs96 = . if demjobs96 >= 8
{txt}(44 real changes made, 44 to missing)

{com}. recode demjobs96 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demjobs96: 553 changes made)

{com}. 
. gen pdiffjobs96 = abs(repjobs96 - demjobs96)
{txt}(65 missing values generated)

{com}. 
. 
. * Aid to blacks
. gen selfaid92 = V923724
{txt}
{com}. replace selfaid92 = . if selfaid92 == 0
{txt}(44 real changes made, 44 to missing)

{com}. replace selfaid92 = . if selfaid92 >= 8
{txt}(8 real changes made, 8 to missing)

{com}. recode selfaid92 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfaid92: 545 changes made)

{com}. label define aidlab -3 "Government should help minority groups" ///
>         3 "Minority groups should help themselves"
{txt}
{com}. label values selfaid92 aidlab
{txt}
{com}. 
. 
. gen selfaid96 = V960487
{txt}
{com}. replace selfaid96 = . if selfaid96 == 0
{txt}(47 real changes made, 47 to missing)

{com}. replace selfaid96 = . if selfaid96 >= 8
{txt}(2 real changes made, 2 to missing)

{com}. recode selfaid96 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(selfaid96: 548 changes made)

{com}. 
. gen repaid96 = V960492
{txt}
{com}. replace repaid96 = . if repaid96 == 0
{txt}(0 real changes made)

{com}. replace repaid96 = . if repaid96 >= 8
{txt}(82 real changes made, 82 to missing)

{com}. recode repaid96 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(repaid96: 515 changes made)

{com}. 
. gen demaid96 = V960490
{txt}
{com}. replace demaid96 = . if demaid96 == 0
{txt}(0 real changes made)

{com}. replace demaid96 = . if demaid96 >= 8
{txt}(57 real changes made, 57 to missing)

{com}. recode demaid96 (1=-3) (2=-2) (3=-1) (4=0) (5=1) (6=2) (7=3)
{txt}(demaid96: 540 changes made)

{com}. 
. gen pdiffaid96 = abs(repaid96 - demaid96)
{txt}(87 missing values generated)

{com}. 
. 
. * Perceived polarization
. alpha pdiffservice92 pdiffdefense92 pdiffjobs92, gen(ppol92)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .8551505
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.6720
{txt}
{com}. 
. alpha pdiffaid96 pdiffjobs96 pdiffinsure96 pdiffdefense96 ///
>         pdiffservice96, gen(ppol96) 

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} 1.287496
{txt}Number of items in the scale:{col 34}{res}        5
{txt}Scale reliability coefficient:{col 34}{res}   0.8612
{txt}
{com}. 
. * Issue extremity
. gen issex1 = abs(selfdefense92 - 0)
{txt}(60 missing values generated)

{com}. gen issex2 = abs(selfservice92 - 0)     
{txt}(79 missing values generated)

{com}. gen issex3 = abs(selfaid92 - 0) 
{txt}(52 missing values generated)

{com}. gen issex4 = abs(selfinsure92 - 0)      
{txt}(75 missing values generated)

{com}. gen issex5 = abs(selfjobs92 - 0)        
{txt}(55 missing values generated)

{com}.         
. alpha issex1-issex5, gen(issextreme92)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .2120501
{txt}Number of items in the scale:{col 34}{res}        5
{txt}Scale reliability coefficient:{col 34}{res}   0.5345
{txt}
{com}. 
. 
. * Do whatever is necessary for equal opportunity
. * Note: This variable is reverse coded so that higher values indicate
. * more conservative attitudes
. gen equalopp92 = V926024 - 1
{txt}
{com}. gen equalopp94 = V940914 - 1
{txt}
{com}. gen equalopp96 = V961229 - 1
{txt}
{com}. 
. replace equalopp92 = . if equalopp92 > 4
{txt}(1 real change made, 1 to missing)

{com}. replace equalopp94 = . if equalopp94 > 4
{txt}(1 real change made, 1 to missing)

{com}. replace equalopp96 = . if equalopp96 > 4
{txt}(2 real changes made, 2 to missing)

{com}. 
. label define equalopportunity 0 "0 Agree strongly" 1 "1 Agree somewhat" ///
>         2 "2 Neither agree nor disagree" 3 "3 Disagree somewhat" ///
>         4 "4 Disagree strongly"
{txt}
{com}. label values equalopp92 equalopportunity
{txt}
{com}. label values equalopp94 equalopportunity
{txt}
{com}. label values equalopp96 equalopportunity
{txt}
{com}. 
. 
. * Have gone too far pushing equal rights
. * Note: This variable is reverse coded so that higher values indicate
. * more conservative attitudes
. gen equalrights92 = V926025
{txt}
{com}. gen equalrights94 = V940915
{txt}
{com}. gen equalrights96 = V961230
{txt}
{com}. 
. replace equalrights92 = . if equalrights92 > 5
{txt}(4 real changes made, 4 to missing)

{com}. recode equalrights92 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(equalrights92: 593 changes made)

{com}. replace equalrights94 = . if equalrights94 > 5
{txt}(1 real change made, 1 to missing)

{com}. recode equalrights94 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(equalrights94: 596 changes made)

{com}. replace equalrights96 = . if equalrights96 > 5
{txt}(4 real changes made, 4 to missing)

{com}. recode equalrights96 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(equalrights96: 541 changes made)

{com}. 
. label define equalrightspush 0 "0 Disagree strongly" 1 "1 Disagree somewhat" /// 
>         2 "2 Neither agree nor disagree" 3 "3 Agree somewhat" 4 "4 Agree strongly"
{txt}
{com}. label values equalrights92 equalrightspush
{txt}
{com}. label values equalrights94 equalrightspush
{txt}
{com}. label values equalrights96 equalrightspush
{txt}
{com}. 
. 
. * Big problem is not giving everyone an equal chance*
. gen equalchance92 = V926029 - 1
{txt}
{com}. gen equalchance94 = V940916 - 1
{txt}
{com}. gen equalchance96 = V961231 - 1
{txt}
{com}. 
. replace equalchance92 = . if equalchance92 > 4
{txt}(2 real changes made, 2 to missing)

{com}. replace equalchance94 = . if equalchance94 > 4
{txt}(4 real changes made, 4 to missing)

{com}. replace equalchance96 = . if equalchance96 > 4
{txt}(1 real change made, 1 to missing)

{com}. 
. label define equalchances 0 "0 Agree strongly" 1 "1 Agree somewhat" ///
>         2 "2 Neither agree nor disagree" 3 "3 Disagree somewhat" ///
>         4 "4 Disagree strongly"
{txt}
{com}. label values equalchance92 equalchances
{txt}
{com}. label values equalchance94 equalchances
{txt}
{com}. label values equalchance96 equalchances
{txt}
{com}. 
. 
. * Better off if we worried less about equality
. * Note: This variable is reverse coded so that higher values indicate
. * more conservative attitudes
. gen lessequal92 = V926026
{txt}
{com}. gen lessequal94 = V940917
{txt}
{com}. gen lessequal96 = V961232
{txt}
{com}. 
. replace lessequal92 = . if lessequal92 > 5
{txt}(4 real changes made, 4 to missing)

{com}. recode lessequal92 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(lessequal92: 593 changes made)

{com}. replace lessequal94 = . if lessequal94 > 5
{txt}(3 real changes made, 3 to missing)

{com}. recode lessequal94 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(lessequal94: 594 changes made)

{com}. replace lessequal96 = . if lessequal96 > 5
{txt}(2 real changes made, 2 to missing)

{com}. recode lessequal96 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(lessequal96: 543 changes made)

{com}. 
. label define lessequality 0 "0 Disagree strongly" 1 "1 Disagree somewhat" /// 
>         2 "2 Neither agree nor disagree" 3 "3 Agree somewhat" 4 "4 Agree strongly"
{txt}
{com}. label values lessequal92 lessequality
{txt}
{com}. label values lessequal94 lessequality
{txt}
{com}. label values lessequal96 lessequality
{txt}
{com}. 
. 
. * Not that big of a problem if people have more of a chance
. * Note: This variable is reverse coded so that higher values indicate
. * more conservative attitudes ???????
. gen unequal92 = V926027
{txt}
{com}. gen unequal94 = V940918
{txt}
{com}. gen unequal96 = V961233
{txt}
{com}. 
. replace unequal92 = . if unequal92 > 5
{txt}(5 real changes made, 5 to missing)

{com}. recode unequal92 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(unequal92: 592 changes made)

{com}. replace unequal94 = . if unequal94 > 5
{txt}(2 real changes made, 2 to missing)

{com}. recode unequal94 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(unequal94: 595 changes made)

{com}. replace unequal96 = . if unequal96 > 5
{txt}(2 real changes made, 2 to missing)

{com}. recode unequal96 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(unequal96: 543 changes made)

{com}. 
. label define unequalchance 0 "0 Disagree strongly" 1 "1 Disagree somewhat" ///
>         2 "2 Neither agree nor disagree" 3 "3 Agree somewhat" 4 "4 Agree strongly"
{txt}
{com}. label values unequal92 unequalchance
{txt}
{com}. label values unequal94 unequalchance
{txt}
{com}. label values unequal96 unequalchance
{txt}
{com}. 
. 
. * Many fewer problems if people were treated equally
. gen fewer92 = V926028 - 1
{txt}
{com}. gen fewer94 = V940919 - 1
{txt}
{com}. gen fewer96 = V961234 - 1
{txt}
{com}. 
. replace fewer92 = . if fewer92 > 4
{txt}(5 real changes made, 5 to missing)

{com}. replace fewer94 = . if fewer94 > 4
{txt}(2 real changes made, 2 to missing)

{com}. replace fewer96 = . if fewer96 > 4
{txt}(1 real change made, 1 to missing)

{com}. 
. label define fewerproblems04 0 "0 Agree strongly" 1 "1 Agree somewhat" ///
>         2 "2 Neither agree nor disagree" 3 "3 Disagree somewhat" ///
>         4 "4 Disagree strongly"
{txt}
{com}. label values fewer92 fewerproblems04
{txt}
{com}. label values fewer94 fewerproblems04
{txt}
{com}. label values fewer96 fewerproblems04
{txt}
{com}. 
. 
. * Adjusting views of moral behavior
. gen changing92 = V926115 - 1
{txt}
{com}. gen changing94 = V941030 - 1
{txt}
{com}. gen changing96 = V961248 - 1
{txt}
{com}. 
. replace changing92 = . if changing92 > 4
{txt}(4 real changes made, 4 to missing)

{com}. replace changing94 = . if changing94 > 4
{txt}(2 real changes made, 2 to missing)

{com}. replace changing96 = . if changing96 > 4
{txt}(3 real changes made, 3 to missing)

{com}. 
. label define changingmorals 0 "Agree strongly" 1 "Agree somewhat" ///
>         2 "Neither agree nor disagree" 3 "Disagree somewhat" 4 "Disagree strongly"
{txt}
{com}. label values changing92 changingmorals
{txt}
{com}. label values changing94 changingmorals
{txt}
{com}. label values changing96 changingmorals
{txt}
{com}.  
.  
. * Newer lifestyles contributing to a breakdown in society
. * Note: This variable is reverse coded so that higher values indicate*
. * more conservative attitudes
. gen lifestyles92 = V926118
{txt}
{com}. gen lifestyles94 = V941029
{txt}
{com}. gen lifestyles96 = V961247
{txt}
{com}. 
. replace lifestyles92 = . if lifestyles92 > 5
{txt}(7 real changes made, 7 to missing)

{com}. recode lifestyles92 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(lifestyles92: 590 changes made)

{com}. replace lifestyles94 = . if lifestyles94 > 5
{txt}(7 real changes made, 7 to missing)

{com}. recode lifestyles94 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(lifestyles94: 590 changes made)

{com}. replace lifestyles96 = . if lifestyles96 > 5
{txt}(6 real changes made, 6 to missing)

{com}. recode lifestyles96 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(lifestyles96: 539 changes made)

{com}. 
. 
. label define lifestylesnew 0 "0 Disagree strongly" 1 "1 Disagree somewhat" /// 
>         2 "2 Neither agree nor disagree"  3 "3 Agree somewhat" 4 "4 Agree strongly"
{txt}
{com}. label values lifestyles92 lifestylesnew
{txt}
{com}. label values lifestyles94 lifestylesnew
{txt}
{com}. label values lifestyles96 lifestylesnew
{txt}
{com}. 
. 
. * Tolerant of people who choose to live according to their own moral standards
. gen standards92 = V926116 - 1
{txt}
{com}. gen standards94 = V941032 - 1
{txt}
{com}. gen standards96 = V961250 - 1
{txt}
{com}. 
. replace standards92 = . if standards92 > 4
{txt}(3 real changes made, 3 to missing)

{com}. replace standards94 = . if standards94 > 4
{txt}(2 real changes made, 2 to missing)

{com}. replace standards96 = . if standards96 > 4
{txt}(5 real changes made, 5 to missing)

{com}. 
. label define standardsown 0 "0 Agree strongly" 1 "1 Agree somewhat" ///
>         2 "2 Neither agree nor disagree" 3 "3 Disagree somewhat" ///
>         4 "4 Disagree strongly"
{txt}
{com}. label values standards92 standardsown
{txt}
{com}. label values standards94 standardsown
{txt}
{com}. label values standards96 standardsown
{txt}
{com}.  
.  
. * More emphasis on traditional family ties*
. * Note: This variable is reverse coded so that higher values indicate
. * more conservative attitudes
. gen family92 = V926117
{txt}
{com}. gen family94 = V941031
{txt}
{com}. gen family96 = V961249
{txt}
{com}. 
. replace family92 = . if family92 > 5
{txt}(6 real changes made, 6 to missing)

{com}. recode family92 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(family92: 591 changes made)

{com}. replace family94 = . if family94 > 5
{txt}(3 real changes made, 3 to missing)

{com}. recode family94 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(family94: 594 changes made)

{com}. replace family96 = . if family96 > 5
{txt}(4 real changes made, 4 to missing)

{com}. recode family96 (5=0) (4=1) (3=2) (2=3) (1=4)
{txt}(family96: 541 changes made)

{com}. 
. 
. label define familyties 0 "0 Disagree strongly" 1 "1 Disagree somewhat" /// 
>         2 "2 Neither agree nor disagree"  3 "3 Agree somewhat" 4 "4 Agree strongly"
{txt}
{com}. label values family92 familyties
{txt}
{com}. label values family94 familyties
{txt}
{com}. label values family96 familyties
{txt}
{com}. 
. 
. * Creating values scales
. alpha equalopp92 equalrights92 equalchance92 lessequal92 unequal92 fewer92 ///
>         changing92 lifestyles92 standards92 family92, detail item ///
>         generate(valuescale92) 

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
equalopp92{col 14}{c |}{res}{col 16} 596{col 24}+{col 31} 0.3443{col 45} 0.2336{col 59} .4092562{col 73} 0.7392
{txt}equalrigh~92{col 14}{c |}{res}{col 16} 593{col 24}+{col 31} 0.6408{col 45} 0.4969{col 59} .3261679{col 73} 0.7037
{txt}equalchan~92{col 14}{c |}{res}{col 16} 595{col 24}+{col 31} 0.5846{col 45} 0.4367{col 59}  .344931{col 73} 0.7145
{txt}lessequal92{col 14}{c |}{res}{col 16} 593{col 24}+{col 31} 0.6537{col 45} 0.5082{col 59}  .321504{col 73} 0.7016
{txt}unequal92{col 14}{c |}{res}{col 16} 592{col 24}+{col 31} 0.5297{col 45} 0.3781{col 59}  .360835{col 73} 0.7235
{txt}fewer92{col 14}{c |}{res}{col 16} 592{col 24}+{col 31} 0.5116{col 45} 0.3690{col 59} .3673637{col 73} 0.7246
{txt}changing92{col 14}{c |}{res}{col 16} 593{col 24}+{col 31} 0.5105{col 45} 0.3249{col 59} .3623429{col 73} 0.7355
{txt}lifestyles92{col 14}{c |}{res}{col 16} 590{col 24}+{col 31} 0.5853{col 45} 0.4462{col 59} .3479695{col 73} 0.7137
{txt}standards92{col 14}{c |}{res}{col 16} 594{col 24}+{col 31} 0.5737{col 45} 0.4247{col 59} .3494101{col 73} 0.7172
{txt}family92{col 14}{c |}{res}{col 16} 591{col 24}+{col 31} 0.5073{col 45} 0.3797{col 59}   .37276{col 73} 0.7235
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59}  .356251{col 73} 0.7409
{txt}{hline 13}{c BT}{hline 65}

Interitem covariances (obs=pairwise, see below)

                  equalopp92  equalrights92  equalchance92    lessequal92
   equalopp92  {res}       0.6434
{txt}equalrights92  {res}       0.1590         1.9565
{txt}equalchance92  {res}       0.3019         0.4527         1.7473
{txt}  lessequal92  {res}       0.2202         1.0973         0.5038         2.0488
{txt}    unequal92  {res}       0.1019         0.6588         0.3934         0.8205
{txt}      fewer92  {res}       0.2508         0.3222         0.8311         0.4183
{txt}   changing92  {res}       0.1074         0.2714         0.3817         0.2378
{txt} lifestyles92  {res}       0.0334         0.5478         0.2397         0.4735
{txt}  standards92  {res}       0.1000         0.3300         0.3620         0.3483
{txt}     family92  {res}       0.0313         0.4508         0.1447         0.3372

               {txt}    unequal92        fewer92     changing92   lifestyles92
    unequal92  {res}       1.6009
{txt}      fewer92  {res}       0.3298         1.4018
{txt}   changing92  {res}      -0.0449         0.2969         2.2043
{txt} lifestyles92  {res}       0.3891         0.0669         0.4746         1.5803
{txt}  standards92  {res}       0.1668         0.2526         0.9648         0.5594
{txt}     family92  {res}       0.2268         0.0331         0.2963         0.7223

               {txt}  standards92       family92
  standards92  {res}       1.7142
{txt}     family92  {res}       0.3690         1.1233

{txt}Pairwise number of observations

                  equalopp92  equalrights92  equalchance92    lessequal92
   equalopp92  {res}          596
{txt}equalrights92  {res}          593            593
{txt}equalchance92  {res}          595            592            595
{txt}  lessequal92  {res}          593            591            593            593
{txt}    unequal92  {res}          592            589            591            590
{txt}      fewer92  {res}          592            590            592            590
{txt}   changing92  {res}          593            590            592            590
{txt} lifestyles92  {res}          590            589            589            587
{txt}  standards92  {res}          594            591            593            591
{txt}     family92  {res}          591            590            590            589

               {txt}    unequal92        fewer92     changing92   lifestyles92
    unequal92  {res}          592
{txt}      fewer92  {res}          588            592
{txt}   changing92  {res}          590            589            593
{txt} lifestyles92  {res}          587            587            588            590
{txt}  standards92  {res}          590            590            592            588
{txt}     family92  {res}          589            588            588            588

               {txt}  standards92       family92
  standards92  {res}          594
{txt}     family92  {res}          589            591
{txt}
{com}. 
. alpha equalopp94 equalrights94 equalchance94 lessequal94 unequal94 fewer94 ///
>         changing94 lifestyles94 standards94 family94, detail item ///
>         generate(valuescale94) 

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
equalopp94{col 14}{c |}{res}{col 16} 596{col 24}+{col 31} 0.4750{col 45} 0.3481{col 59} .3717626{col 73} 0.7278
{txt}equalrigh~94{col 14}{c |}{res}{col 16} 596{col 24}+{col 31} 0.6375{col 45} 0.4925{col 59} .3210498{col 73} 0.7060
{txt}equalchan~94{col 14}{c |}{res}{col 16} 593{col 24}+{col 31} 0.5498{col 45} 0.3887{col 59} .3451871{col 73} 0.7232
{txt}lessequal94{col 14}{c |}{res}{col 16} 594{col 24}+{col 31} 0.5961{col 45} 0.4436{col 59} .3319458{col 73} 0.7140
{txt}unequal94{col 14}{c |}{res}{col 16} 595{col 24}+{col 31} 0.5157{col 45} 0.3580{col 59} .3546428{col 73} 0.7266
{txt}fewer94{col 14}{c |}{res}{col 16} 595{col 24}+{col 31} 0.6063{col 45} 0.4677{col 59} .3330492{col 73} 0.7103
{txt}changing94{col 14}{c |}{res}{col 16} 595{col 24}+{col 31} 0.5149{col 45} 0.3386{col 59} .3520999{col 73} 0.7318
{txt}lifestyles94{col 14}{c |}{res}{col 16} 590{col 24}+{col 31} 0.5113{col 45} 0.3669{col 59} .3587908{col 73} 0.7258
{txt}standards94{col 14}{c |}{res}{col 16} 595{col 24}+{col 31} 0.5870{col 45} 0.4450{col 59} .3381364{col 73} 0.7140
{txt}family94{col 14}{c |}{res}{col 16} 594{col 24}+{col 31} 0.4847{col 45} 0.3667{col 59} .3717463{col 73} 0.7263
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .3478432{col 73} 0.7415
{txt}{hline 13}{c BT}{hline 65}

Interitem covariances (obs=pairwise, see below)

                  equalopp94  equalrights94  equalchance94    lessequal94
   equalopp94  {res}       0.9964
{txt}equalrights94  {res}       0.2636         1.8845
{txt}equalchance94  {res}       0.4261         0.3782         1.7980
{txt}  lessequal94  {res}       0.3428         0.8992         0.3743         1.8457
{txt}    unequal94  {res}       0.1953         0.6626         0.2471         0.7235
{txt}      fewer94  {res}       0.4669         0.5188         0.8610         0.4305
{txt}   changing94  {res}       0.1850         0.2705         0.4342         0.0337
{txt} lifestyles94  {res}       0.0754         0.4027         0.0191         0.3327
{txt}  standards94  {res}       0.2645         0.3583         0.3556         0.2679
{txt}     family94  {res}       0.0508         0.3392         0.1277         0.2981

               {txt}    unequal94        fewer94     changing94   lifestyles94
    unequal94  {res}       1.6005
{txt}      fewer94  {res}       0.3097         1.6053
{txt}   changing94  {res}       0.0685         0.4504         1.9799
{txt} lifestyles94  {res}       0.2754         0.1380         0.4308         1.3928
{txt}  standards94  {res}       0.2027         0.3974         0.8508         0.4636
{txt}     family94  {res}       0.2005         0.0889         0.2541         0.5942

               {txt}  standards94       family94
  standards94  {res}       1.5869
{txt}     family94  {res}       0.3187         0.9128

{txt}Pairwise number of observations

                  equalopp94  equalrights94  equalchance94    lessequal94
   equalopp94  {res}          596
{txt}equalrights94  {res}          595            596
{txt}equalchance94  {res}          592            592            593
{txt}  lessequal94  {res}          593            593            590            594
{txt}    unequal94  {res}          594            594            591            592
{txt}      fewer94  {res}          594            594            591            593
{txt}   changing94  {res}          594            594            591            593
{txt} lifestyles94  {res}          589            589            586            588
{txt}  standards94  {res}          594            594            591            594
{txt}     family94  {res}          593            593            590            593

               {txt}    unequal94        fewer94     changing94   lifestyles94
    unequal94  {res}          595
{txt}      fewer94  {res}          593            595
{txt}   changing94  {res}          593            593            595
{txt} lifestyles94  {res}          588            588            590            590
{txt}  standards94  {res}          593            594            594            589
{txt}     family94  {res}          592            593            593            588

               {txt}  standards94       family94
  standards94  {res}          595
{txt}     family94  {res}          594            594
{txt}
{com}. 
. alpha equalopp96 equalrights96 equalchance96 lessequal96 unequal96 fewer96 ///
>         changing96 lifestyles96 standards96 family96, detail item ///
>         generate(valuescale96) 

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
equalopp96{col 14}{c |}{res}{col 16} 595{col 24}+{col 31} 0.6683{col 45} 0.5958{col 59} .9001259{col 73} 0.8834
{txt}equalrigh~96{col 14}{c |}{res}{col 16} 593{col 24}+{col 31} 0.7335{col 45} 0.6532{col 59} .8410704{col 73} 0.8791
{txt}equalchan~96{col 14}{c |}{res}{col 16} 596{col 24}+{col 31} 0.7500{col 45} 0.6728{col 59} .8339575{col 73} 0.8777
{txt}lessequal96{col 14}{c |}{res}{col 16} 595{col 24}+{col 31} 0.6846{col 45} 0.5954{col 59} .8645584{col 73} 0.8829
{txt}unequal96{col 14}{c |}{res}{col 16} 595{col 24}+{col 31} 0.6227{col 45} 0.5327{col 59} .9009889{col 73} 0.8871
{txt}fewer96{col 14}{c |}{res}{col 16} 596{col 24}+{col 31} 0.7149{col 45} 0.6336{col 59} .8551168{col 73} 0.8805
{txt}changing96{col 14}{c |}{res}{col 16} 594{col 24}+{col 31} 0.7599{col 45} 0.6769{col 59}  .812274{col 73} 0.8775
{txt}lifestyles96{col 14}{c |}{res}{col 16} 591{col 24}+{col 31} 0.6872{col 45} 0.6000{col 59} .8635314{col 73} 0.8828
{txt}standards96{col 14}{c |}{res}{col 16} 592{col 24}+{col 31} 0.7416{col 45} 0.6665{col 59} .8432877{col 73} 0.8781
{txt}family96{col 14}{c |}{res}{col 16} 593{col 24}+{col 31} 0.7457{col 45} 0.6766{col 59} .8540032{col 73} 0.8777
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .8568893{col 73} 0.8914
{txt}{hline 13}{c BT}{hline 65}

Interitem covariances (obs=pairwise, see below)

                  equalopp96  equalrights96  equalchance96    lessequal96
   equalopp96  {res}       1.2883
{txt}equalrights96  {res}       0.6605         2.0728
{txt}equalchance96  {res}       0.9044         0.9766         2.0918
{txt}  lessequal96  {res}       0.5994         1.2217         0.8126         1.9408
{txt}    unequal96  {res}       0.5361         0.8765         0.7562         0.9316
{txt}      fewer96  {res}       0.8408         0.8794         1.3147         0.7343
{txt}   changing96  {res}       0.8414         0.9488         1.2416         0.7916
{txt} lifestyles96  {res}       0.5032         0.8696         0.7691         0.7376
{txt}  standards96  {res}       0.6976         0.9270         0.9642         0.7074
{txt}     family96  {res}       0.5733         0.9224         0.7941         0.9004

               {txt}    unequal96        fewer96     changing96   lifestyles96
    unequal96  {res}       1.6126
{txt}      fewer96  {res}       0.6642         1.9104
{txt}   changing96  {res}       0.5828         1.1209         2.5165
{txt} lifestyles96  {res}       0.5472         0.6494         1.1827         1.9521
{txt}  standards96  {res}       0.5416         0.8953         1.4588         1.0560
{txt}     family96  {res}       0.6886         0.6744         1.1513         1.1580

               {txt}  standards96       family96
  standards96  {res}       1.9394
{txt}     family96  {res}       0.9557         1.6875

{txt}Pairwise number of observations

                  equalopp96  equalrights96  equalchance96    lessequal96
   equalopp96  {res}          595
{txt}equalrights96  {res}          592            593
{txt}equalchance96  {res}          595            593            596
{txt}  lessequal96  {res}          594            592            595            595
{txt}    unequal96  {res}          594            592            595            594
{txt}      fewer96  {res}          595            593            596            595
{txt}   changing96  {res}          594            591            594            593
{txt} lifestyles96  {res}          591            591            591            590
{txt}  standards96  {res}          592            590            592            591
{txt}     family96  {res}          593            591            593            593

               {txt}    unequal96        fewer96     changing96   lifestyles96
    unequal96  {res}          595
{txt}      fewer96  {res}          595            596
{txt}   changing96  {res}          593            594            594
{txt} lifestyles96  {res}          590            591            590            591
{txt}  standards96  {res}          591            592            591            590
{txt}     family96  {res}          592            593            592            590

               {txt}  standards96       family96
  standards96  {res}          592
{txt}     family96  {res}          590            593
{txt}
{com}.         
.         
. * Value polarization    
. gen valuepold92 = .
{txt}(597 missing values generated)

{com}. gen valuepolr92 = .     
{txt}(597 missing values generated)

{com}. gen valuepold94 = .
{txt}(597 missing values generated)

{com}. gen valuepolr94 = .     
{txt}(597 missing values generated)

{com}. gen valuepold96 = .
{txt}(597 missing values generated)

{com}. gen valuepolr96 = .     
{txt}(597 missing values generated)

{com}.         
. sum valuescale92 if valuescale92 != . & rep92 == 1, meanonly
{txt}
{com}. replace valuepold92 = valuescale92 - r(mean) if valuescale92 != . & rep92 == 0
{txt}(191 real changes made)

{com}. sum valuescale92 if valuescale92 != . & rep92 == 0, meanonly
{txt}
{com}. replace valuepolr92 = valuescale92 - r(mean) if valuescale92 != . & rep92 == 1
{txt}(167 real changes made)

{com}. 
. sum valuescale94 if valuescale94 != . & rep94 == 1, meanonly
{txt}
{com}. replace valuepold94 = valuescale94 - r(mean) if valuescale94 != . & rep94 == 0
{txt}(204 real changes made)

{com}. sum valuescale94 if valuescale94 != . & rep94 == 0, meanonly
{txt}
{com}. replace valuepolr94 = valuescale94 - r(mean) if valuescale94 != . & rep94 == 1
{txt}(192 real changes made)

{com}. 
. sum valuescale96 if valuescale96 != . & rep96 == 1, meanonly
{txt}
{com}. replace valuepold96 = valuescale96 - r(mean) if valuescale96 != . & rep96 == 0
{txt}(222 real changes made)

{com}. sum valuescale96 if valuescale96 != . & rep96 == 0, meanonly
{txt}
{com}. replace valuepolr96 = valuescale96 - r(mean) if valuescale96 != . & rep96 == 1
{txt}(181 real changes made)

{com}. 
. foreach v of var valuepold92 - valuepolr96{c -(} 
{txt}  2{com}.         replace `v' = abs(`v')
{txt}  3{com}. {c )-}
{txt}(158 real changes made)
(31 real changes made)
(181 real changes made)
(18 real changes made)
(191 real changes made)
(19 real changes made)

{com}. 
. egen valuepol92 = rowtotal(valuepold92 valuepolr92)
{txt}
{com}. egen valuepol94 = rowtotal(valuepold94 valuepolr94)
{txt}
{com}. egen valuepol96 = rowtotal(valuepold96 valuepolr96)
{txt}
{com}. 
. ********************************************************************************
. 
. ****
. ** Table 2
. ****
. 
. * Model 1
. sem (valuepol96 <- valuepol92 partydifftherm92 sorting92 issextreme92 ///
>         interest92 edu92 age92 income92 church92 female92 white92 black92 south92) ///
>         (partydifftherm96 <- valuepol92 partydifftherm92 sorting92 issextreme92 ///
>         interest92 edu92 age92 income92 church92 female92 white92 black92 south92), ///
>         standardized method(mlmv) 
{res}{txt}{p 0 6 2}note: Missing values found in observed exogenous variables. Using the {opt noxconditional} behavior. Specify the {opt forcexconditional} option to override this behavior.{p_end}
Endogenous variables

{p 0 11 2}Observed:{space 2}{res}valuepol96 partydifftherm96{p_end}
{txt}
Exogenous variables

{p 0 11 2}Observed:{space 2}{res}valuepol92 partydifftherm92 sorting92 issextreme92 interest92 edu92 age92 income92 church92 female92 white92 black92 south92{p_end}
{txt}
Fitting saturated model:

Iteration 0:{space 3}log likelihood = {res:-16387.369}  
Iteration 1:{space 3}log likelihood = {res:-16317.818}  
Iteration 2:{space 3}log likelihood = {res:-16284.043}  
Iteration 3:{space 3}log likelihood = {res:-16283.059}  
Iteration 4:{space 3}log likelihood = {res:-16283.054}  
Iteration 5:{space 3}log likelihood = {res:-16283.054}  

Fitting baseline model:

Iteration 0:{space 3}log likelihood = {res:-16424.905}  
Iteration 1:{space 3}log likelihood = {res:-16424.828}  
Iteration 2:{space 3}log likelihood = {res:-16424.828}  
{res}{txt}
Fitting target model:

Iteration 0:{space 3}log likelihood = {res:-16308.981}  
Iteration 1:{space 3}log likelihood = {res:-16308.884}  
Iteration 2:{space 3}log likelihood = {res:-16308.884}  

{col 1}Structural equation model{col 49}Number of obs{col 67}= {res}       597
{txt}{col 1}Estimation method{col 20}= {res}mlmv
{txt}{col 1}Log likelihood{col 20}= {res}-16308.884

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}      OIM
{col 1}   Standardized{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Structural     {col 17}{txt}{c |}
{space 2}{col 3}valuepol96   {col 17}{c |}
{space 5}valuepol92 {c |}{col 17}{res}{space 2} .1843817{col 29}{space 2}  .047723{col 40}{space 1}    3.86{col 49}{space 3}0.000{col 57}{space 4} .0908463{col 70}{space 3} .2779171
{txt}{space 2}partydifft~92 {c |}{col 17}{res}{space 2} .0628057{col 29}{space 2} .0439806{col 40}{space 1}    1.43{col 49}{space 3}0.153{col 57}{space 4}-.0233946{col 70}{space 3}  .149006
{txt}{space 6}sorting92 {c |}{col 17}{res}{space 2} .1342685{col 29}{space 2} .0526998{col 40}{space 1}    2.55{col 49}{space 3}0.011{col 57}{space 4} .0309788{col 70}{space 3} .2375582
{txt}{space 3}issextreme92 {c |}{col 17}{res}{space 2} .0040773{col 29}{space 2} .0407165{col 40}{space 1}    0.10{col 49}{space 3}0.920{col 57}{space 4}-.0757255{col 70}{space 3} .0838801
{txt}{space 5}interest92 {c |}{col 17}{res}{space 2} .0094963{col 29}{space 2} .0416867{col 40}{space 1}    0.23{col 49}{space 3}0.820{col 57}{space 4}-.0722082{col 70}{space 3} .0912008
{txt}{space 10}edu92 {c |}{col 17}{res}{space 2} .0087356{col 29}{space 2} .0493365{col 40}{space 1}    0.18{col 49}{space 3}0.859{col 57}{space 4}-.0879621{col 70}{space 3} .1054334
{txt}{space 10}age92 {c |}{col 17}{res}{space 2}-.0759032{col 29}{space 2} .0400812{col 40}{space 1}   -1.89{col 49}{space 3}0.058{col 57}{space 4}-.1544609{col 70}{space 3} .0026544
{txt}{space 7}income92 {c |}{col 17}{res}{space 2} .0442988{col 29}{space 2} .0485167{col 40}{space 1}    0.91{col 49}{space 3}0.361{col 57}{space 4}-.0507921{col 70}{space 3} .1393898
{txt}{space 7}church92 {c |}{col 17}{res}{space 2} .0439927{col 29}{space 2} .0448424{col 40}{space 1}    0.98{col 49}{space 3}0.327{col 57}{space 4}-.0438967{col 70}{space 3} .1318822
{txt}{space 7}female92 {c |}{col 17}{res}{space 2} .0410832{col 29}{space 2}  .040133{col 40}{space 1}    1.02{col 49}{space 3}0.306{col 57}{space 4}-.0375761{col 70}{space 3} .1197425
{txt}{space 8}white92 {c |}{col 17}{res}{space 2}-.0011689{col 29}{space 2} .0828336{col 40}{space 1}   -0.01{col 49}{space 3}0.989{col 57}{space 4}-.1635198{col 70}{space 3} .1611819
{txt}{space 8}black92 {c |}{col 17}{res}{space 2}  .039132{col 29}{space 2} .0828058{col 40}{space 1}    0.47{col 49}{space 3}0.637{col 57}{space 4}-.1231643{col 70}{space 3} .2014283
{txt}{space 8}south92 {c |}{col 17}{res}{space 2}-.0145803{col 29}{space 2} .0401022{col 40}{space 1}   -0.36{col 49}{space 3}0.716{col 57}{space 4}-.0931792{col 70}{space 3} .0640187
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3695542{col 29}{space 2} .3475326{col 40}{space 1}    1.06{col 49}{space 3}0.288{col 57}{space 4}-.3115972{col 70}{space 3} 1.050706
{space 2}{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}partydifft~96{col 17}{c |}
{space 5}valuepol92 {c |}{col 17}{res}{space 2} .1181193{col 29}{space 2} .0442967{col 40}{space 1}    2.67{col 49}{space 3}0.008{col 57}{space 4} .0312994{col 70}{space 3} .2049392
{txt}{space 2}partydifft~92 {c |}{col 17}{res}{space 2} .3317726{col 29}{space 2} .0382312{col 40}{space 1}    8.68{col 49}{space 3}0.000{col 57}{space 4} .2568408{col 70}{space 3} .4067044
{txt}{space 6}sorting92 {c |}{col 17}{res}{space 2} .1330294{col 29}{space 2} .0479477{col 40}{space 1}    2.77{col 49}{space 3}0.006{col 57}{space 4} .0390536{col 70}{space 3} .2270052
{txt}{space 3}issextreme92 {c |}{col 17}{res}{space 2} .0881572{col 29}{space 2}  .037892{col 40}{space 1}    2.33{col 49}{space 3}0.020{col 57}{space 4} .0138902{col 70}{space 3} .1624241
{txt}{space 5}interest92 {c |}{col 17}{res}{space 2} .0463396{col 29}{space 2} .0392928{col 40}{space 1}    1.18{col 49}{space 3}0.238{col 57}{space 4}-.0306728{col 70}{space 3}  .123352
{txt}{space 10}edu92 {c |}{col 17}{res}{space 2}-.0420802{col 29}{space 2} .0454096{col 40}{space 1}   -0.93{col 49}{space 3}0.354{col 57}{space 4}-.1310813{col 70}{space 3} .0469209
{txt}{space 10}age92 {c |}{col 17}{res}{space 2} .0295245{col 29}{space 2} .0376866{col 40}{space 1}    0.78{col 49}{space 3}0.433{col 57}{space 4}-.0443398{col 70}{space 3} .1033888
{txt}{space 7}income92 {c |}{col 17}{res}{space 2} .0560198{col 29}{space 2} .0444252{col 40}{space 1}    1.26{col 49}{space 3}0.207{col 57}{space 4}-.0310521{col 70}{space 3} .1430916
{txt}{space 7}church92 {c |}{col 17}{res}{space 2} .0257361{col 29}{space 2} .0405474{col 40}{space 1}    0.63{col 49}{space 3}0.526{col 57}{space 4}-.0537353{col 70}{space 3} .1052076
{txt}{space 7}female92 {c |}{col 17}{res}{space 2} .0567346{col 29}{space 2} .0372605{col 40}{space 1}    1.52{col 49}{space 3}0.128{col 57}{space 4}-.0162946{col 70}{space 3} .1297639
{txt}{space 8}white92 {c |}{col 17}{res}{space 2} .0060939{col 29}{space 2} .0783203{col 40}{space 1}    0.08{col 49}{space 3}0.938{col 57}{space 4} -.147411{col 70}{space 3} .1595988
{txt}{space 8}black92 {c |}{col 17}{res}{space 2}  .011506{col 29}{space 2} .0785254{col 40}{space 1}    0.15{col 49}{space 3}0.884{col 57}{space 4} -.142401{col 70}{space 3} .1654131
{txt}{space 8}south92 {c |}{col 17}{res}{space 2}-.0518598{col 29}{space 2} .0373033{col 40}{space 1}   -1.39{col 49}{space 3}0.164{col 57}{space 4}-.1249728{col 70}{space 3} .0212533
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .0398524{col 29}{space 2} .3267571{col 40}{space 1}    0.12{col 49}{space 3}0.903{col 57}{space 4}-.6005797{col 70}{space 3} .6802846
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mean(valuepol92){c |}{col 17}{res}{space 2} .8532282{col 29}{space 2} .0477991{col 40}{space 1}   17.85{col 49}{space 3}0.000{col 57}{space 4} .7595436{col 70}{space 3} .9469127
{txt}mean(partydi~92){c |}{col 17}{res}{space 2} 1.154705{col 29}{space 2}  .053965{col 40}{space 1}   21.40{col 49}{space 3}0.000{col 57}{space 4} 1.048935{col 70}{space 3} 1.260474
{txt}{space 1}mean(sorting92){c |}{col 17}{res}{space 2} .8731895{col 29}{space 2} .0549132{col 40}{space 1}   15.90{col 49}{space 3}0.000{col 57}{space 4} .7655616{col 70}{space 3} .9808174
{txt}mean(issextr~92){c |}{col 17}{res}{space 2} 2.015097{col 29}{space 2} .0716011{col 40}{space 1}   28.14{col 49}{space 3}0.000{col 57}{space 4} 1.874761{col 70}{space 3} 2.155432
{txt}mean(interest92){c |}{col 17}{res}{space 2} 3.641132{col 29}{space 2} .1132034{col 40}{space 1}   32.16{col 49}{space 3}0.000{col 57}{space 4} 3.419258{col 70}{space 3} 3.863007
{txt}{space 5}mean(edu92){c |}{col 17}{res}{space 2} 2.445968{col 29}{space 2} .0828833{col 40}{space 1}   29.51{col 49}{space 3}0.000{col 57}{space 4} 2.283519{col 70}{space 3} 2.608416
{txt}{space 5}mean(age92){c |}{col 17}{res}{space 2} 2.599566{col 29}{space 2} .0856434{col 40}{space 1}   30.35{col 49}{space 3}0.000{col 57}{space 4} 2.431708{col 70}{space 3} 2.767424
{txt}{space 2}mean(income92){c |}{col 17}{res}{space 2} 2.295662{col 29}{space 2} .0822704{col 40}{space 1}   27.90{col 49}{space 3}0.000{col 57}{space 4} 2.134415{col 70}{space 3} 2.456909
{txt}{space 2}mean(church92){c |}{col 17}{res}{space 2} 3.198208{col 29}{space 2} .1134508{col 40}{space 1}   28.19{col 49}{space 3}0.000{col 57}{space 4} 2.975848{col 70}{space 3} 3.420567
{txt}{space 2}mean(female92){c |}{col 17}{res}{space 2} 1.046297{col 29}{space 2} .0509108{col 40}{space 1}   20.55{col 49}{space 3}0.000{col 57}{space 4} .9465134{col 70}{space 3}  1.14608
{txt}{space 3}mean(white92){c |}{col 17}{res}{space 2} 2.270383{col 29}{space 2}  .077409{col 40}{space 1}   29.33{col 49}{space 3}0.000{col 57}{space 4} 2.118664{col 70}{space 3} 2.422102
{txt}{space 3}mean(black92){c |}{col 17}{res}{space 2} .3876713{col 29}{space 2} .0424372{col 40}{space 1}    9.14{col 49}{space 3}0.000{col 57}{space 4}  .304496{col 70}{space 3} .4708466
{txt}{space 3}mean(south92){c |}{col 17}{res}{space 2}  .758418{col 29}{space 2} .0464412{col 40}{space 1}   16.33{col 49}{space 3}0.000{col 57}{space 4}  .667395{col 70}{space 3}  .849441
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
var(e.value~l96){c |}{col 17}{res}{space 2} .8880731{col 29}{space 2} .0248986{col 57}{space 4} .8405892{col 70}{space 3} .9382393
{txt}var(e.partyd~96){c |}{col 17}{res}{space 2} .7499009{col 29}{space 2} .0313955{col 57}{space 4} .6908238{col 70}{space 3} .8140301
{txt}{space 1}var(valuepol92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}var(partydif~92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 2}var(sorting92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}var(issextre~92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 1}var(interest92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 6}var(edu92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 6}var(age92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 3}var(income92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 3}var(church92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 3}var(female92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 4}var(white92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 4}var(black92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 4}var(south92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}cov(valuepol92,{c |}
partydiffthe~92){c |}{col 17}{res}{space 2} .2963436{col 29}{space 2} .0376713{col 40}{space 1}    7.87{col 49}{space 3}0.000{col 57}{space 4} .2225093{col 70}{space 3}  .370178
{txt}{space 1}cov(valuepol92,{c |}
{space 6}sorting92){c |}{col 17}{res}{space 2} .5120792{col 29}{space 2} .0321998{col 40}{space 1}   15.90{col 49}{space 3}0.000{col 57}{space 4} .4489688{col 70}{space 3} .5751896
{txt}{space 1}cov(valuepol92,{c |}
{space 3}issextreme92){c |}{col 17}{res}{space 2}  .084439{col 29}{space 2} .0407624{col 40}{space 1}    2.07{col 49}{space 3}0.038{col 57}{space 4} .0045462{col 70}{space 3} .1643318
{txt}{space 1}cov(valuepol92,{c |}
{space 5}interest92){c |}{col 17}{res}{space 2} .1473392{col 29}{space 2} .0400473{col 40}{space 1}    3.68{col 49}{space 3}0.000{col 57}{space 4} .0688479{col 70}{space 3} .2258304
{txt}{space 1}cov(valuepol92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2} .1718603{col 29}{space 2} .0399935{col 40}{space 1}    4.30{col 49}{space 3}0.000{col 57}{space 4} .0934745{col 70}{space 3}  .250246
{txt}{space 1}cov(valuepol92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2}-.0040948{col 29}{space 2} .0409266{col 40}{space 1}   -0.10{col 49}{space 3}0.920{col 57}{space 4}-.0843094{col 70}{space 3} .0761199
{txt}{space 1}cov(valuepol92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2}  .083578{col 29}{space 2} .0416054{col 40}{space 1}    2.01{col 49}{space 3}0.045{col 57}{space 4} .0020329{col 70}{space 3} .1651231
{txt}{space 1}cov(valuepol92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} -.032463{col 29}{space 2} .0461312{col 40}{space 1}   -0.70{col 49}{space 3}0.482{col 57}{space 4}-.1228785{col 70}{space 3} .0579525
{txt}{space 1}cov(valuepol92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2} .0849649{col 29}{space 2} .0406318{col 40}{space 1}    2.09{col 49}{space 3}0.037{col 57}{space 4}  .005328{col 70}{space 3} .1646018
{txt}{space 1}cov(valuepol92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}-.0488647{col 29}{space 2} .0408296{col 40}{space 1}   -1.20{col 49}{space 3}0.231{col 57}{space 4}-.1288892{col 70}{space 3} .0311597
{txt}{space 1}cov(valuepol92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .0787568{col 29}{space 2} .0406734{col 40}{space 1}    1.94{col 49}{space 3}0.053{col 57}{space 4}-.0009616{col 70}{space 3} .1584753
{txt}{space 1}cov(valuepol92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} -.028927{col 29}{space 2}  .040893{col 40}{space 1}   -0.71{col 49}{space 3}0.479{col 57}{space 4}-.1090759{col 70}{space 3} .0512218
{txt}cov(partydif~92,{c |}
{space 6}sorting92){c |}{col 17}{res}{space 2} .3071233{col 29}{space 2} .0410977{col 40}{space 1}    7.47{col 49}{space 3}0.000{col 57}{space 4} .2265734{col 70}{space 3} .3876732
{txt}cov(partydif~92,{c |}
{space 3}issextreme92){c |}{col 17}{res}{space 2} .1986419{col 29}{space 2} .0407132{col 40}{space 1}    4.88{col 49}{space 3}0.000{col 57}{space 4} .1188456{col 70}{space 3} .2784383
{txt}cov(partydif~92,{c |}
{space 5}interest92){c |}{col 17}{res}{space 2} .1352886{col 29}{space 2} .0419815{col 40}{space 1}    3.22{col 49}{space 3}0.001{col 57}{space 4} .0530063{col 70}{space 3} .2175709
{txt}cov(partydif~92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2} .0202022{col 29}{space 2} .0426442{col 40}{space 1}    0.47{col 49}{space 3}0.636{col 57}{space 4}-.0633789{col 70}{space 3} .1037833
{txt}cov(partydif~92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2} .0443143{col 29}{space 2} .0418957{col 40}{space 1}    1.06{col 49}{space 3}0.290{col 57}{space 4}-.0377999{col 70}{space 3} .1264284
{txt}cov(partydif~92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2}-.0773039{col 29}{space 2} .0430205{col 40}{space 1}   -1.80{col 49}{space 3}0.072{col 57}{space 4}-.1616225{col 70}{space 3} .0070146
{txt}cov(partydif~92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2}-.0524313{col 29}{space 2} .0479945{col 40}{space 1}   -1.09{col 49}{space 3}0.275{col 57}{space 4}-.1464988{col 70}{space 3} .0416362
{txt}cov(partydif~92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2} .0289791{col 29}{space 2} .0415696{col 40}{space 1}    0.70{col 49}{space 3}0.486{col 57}{space 4}-.0524959{col 70}{space 3} .1104541
{txt}cov(partydif~92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}-.0538762{col 29}{space 2} .0419658{col 40}{space 1}   -1.28{col 49}{space 3}0.199{col 57}{space 4}-.1361275{col 70}{space 3} .0283752
{txt}cov(partydif~92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .0827344{col 29}{space 2} .0419189{col 40}{space 1}    1.97{col 49}{space 3}0.048{col 57}{space 4}  .000575{col 70}{space 3} .1648939
{txt}cov(partydif~92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} .0021997{col 29}{space 2} .0417758{col 40}{space 1}    0.05{col 49}{space 3}0.958{col 57}{space 4}-.0796794{col 70}{space 3} .0840788
{txt}{space 2}cov(sorting92,{c |}
{space 3}issextreme92){c |}{col 17}{res}{space 2} .0648645{col 29}{space 2} .0470723{col 40}{space 1}    1.38{col 49}{space 3}0.168{col 57}{space 4}-.0273956{col 70}{space 3} .1571245
{txt}{space 2}cov(sorting92,{c |}
{space 5}interest92){c |}{col 17}{res}{space 2} .1786912{col 29}{space 2} .0462391{col 40}{space 1}    3.86{col 49}{space 3}0.000{col 57}{space 4} .0880642{col 70}{space 3} .2693183
{txt}{space 2}cov(sorting92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2} .1267969{col 29}{space 2} .0455375{col 40}{space 1}    2.78{col 49}{space 3}0.005{col 57}{space 4} .0375451{col 70}{space 3} .2160487
{txt}{space 2}cov(sorting92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2} .0329739{col 29}{space 2} .0446496{col 40}{space 1}    0.74{col 49}{space 3}0.460{col 57}{space 4}-.0545377{col 70}{space 3} .1204855
{txt}{space 2}cov(sorting92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2} .0686588{col 29}{space 2} .0466297{col 40}{space 1}    1.47{col 49}{space 3}0.141{col 57}{space 4}-.0227338{col 70}{space 3} .1600514
{txt}{space 2}cov(sorting92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2}-.0286712{col 29}{space 2} .0494071{col 40}{space 1}   -0.58{col 49}{space 3}0.562{col 57}{space 4}-.1255073{col 70}{space 3}  .068165
{txt}{space 2}cov(sorting92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.0269115{col 29}{space 2} .0445541{col 40}{space 1}   -0.60{col 49}{space 3}0.546{col 57}{space 4}-.1142359{col 70}{space 3} .0604129
{txt}{space 2}cov(sorting92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .0106748{col 29}{space 2} .0483962{col 40}{space 1}    0.22{col 49}{space 3}0.825{col 57}{space 4}-.0841801{col 70}{space 3} .1055296
{txt}{space 2}cov(sorting92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .0131083{col 29}{space 2}  .049247{col 40}{space 1}    0.27{col 49}{space 3}0.790{col 57}{space 4}-.0834141{col 70}{space 3} .1096307
{txt}{space 2}cov(sorting92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0326775{col 29}{space 2} .0458503{col 40}{space 1}   -0.71{col 49}{space 3}0.476{col 57}{space 4}-.1225424{col 70}{space 3} .0571875
{txt}cov(issextre~92,{c |}
{space 5}interest92){c |}{col 17}{res}{space 2}-.0409241{col 29}{space 2} .0417781{col 40}{space 1}   -0.98{col 49}{space 3}0.327{col 57}{space 4}-.1228076{col 70}{space 3} .0409595
{txt}cov(issextre~92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2}-.0881354{col 29}{space 2} .0414907{col 40}{space 1}   -2.12{col 49}{space 3}0.034{col 57}{space 4}-.1694557{col 70}{space 3}-.0068151
{txt}cov(issextre~92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2} .0101289{col 29}{space 2} .0412267{col 40}{space 1}    0.25{col 49}{space 3}0.806{col 57}{space 4} -.070674{col 70}{space 3} .0909317
{txt}cov(issextre~92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2}-.1121566{col 29}{space 2} .0422967{col 40}{space 1}   -2.65{col 49}{space 3}0.008{col 57}{space 4}-.1950567{col 70}{space 3}-.0292566
{txt}cov(issextre~92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} .0318259{col 29}{space 2} .0452955{col 40}{space 1}    0.70{col 49}{space 3}0.482{col 57}{space 4}-.0569516{col 70}{space 3} .1206035
{txt}cov(issextre~92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2} .0515689{col 29}{space 2} .0410374{col 40}{space 1}    1.26{col 49}{space 3}0.209{col 57}{space 4} -.028863{col 70}{space 3} .1320008
{txt}cov(issextre~92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}-.1059522{col 29}{space 2} .0408398{col 40}{space 1}   -2.59{col 49}{space 3}0.009{col 57}{space 4}-.1859967{col 70}{space 3}-.0259077
{txt}cov(issextre~92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .1246702{col 29}{space 2} .0407562{col 40}{space 1}    3.06{col 49}{space 3}0.002{col 57}{space 4} .0447895{col 70}{space 3}  .204551
{txt}cov(issextre~92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} .0331751{col 29}{space 2} .0412142{col 40}{space 1}    0.80{col 49}{space 3}0.421{col 57}{space 4}-.0476032{col 70}{space 3} .1139534
{txt}{space 1}cov(interest92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2} .2916369{col 29}{space 2} .0381205{col 40}{space 1}    7.65{col 49}{space 3}0.000{col 57}{space 4}  .216922{col 70}{space 3} .3663518
{txt}{space 1}cov(interest92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2}  .027364{col 29}{space 2} .0409188{col 40}{space 1}    0.67{col 49}{space 3}0.504{col 57}{space 4}-.0528354{col 70}{space 3} .1075634
{txt}{space 1}cov(interest92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2} .2066177{col 29}{space 2} .0412618{col 40}{space 1}    5.01{col 49}{space 3}0.000{col 57}{space 4} .1257461{col 70}{space 3} .2874894
{txt}{space 1}cov(interest92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} .0167412{col 29}{space 2} .0469808{col 40}{space 1}    0.36{col 49}{space 3}0.722{col 57}{space 4}-.0753395{col 70}{space 3} .1088219
{txt}{space 1}cov(interest92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.0673802{col 29}{space 2} .0408039{col 40}{space 1}   -1.65{col 49}{space 3}0.099{col 57}{space 4}-.1473544{col 70}{space 3} .0125939
{txt}{space 1}cov(interest92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .0304713{col 29}{space 2} .0409012{col 40}{space 1}    0.74{col 49}{space 3}0.456{col 57}{space 4}-.0496937{col 70}{space 3} .1106362
{txt}{space 1}cov(interest92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.0368772{col 29}{space 2} .0408809{col 40}{space 1}   -0.90{col 49}{space 3}0.367{col 57}{space 4}-.1170024{col 70}{space 3} .0432479
{txt}{space 1}cov(interest92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0022404{col 29}{space 2} .0409982{col 40}{space 1}   -0.05{col 49}{space 3}0.956{col 57}{space 4}-.0825954{col 70}{space 3} .0781145
{txt}cov(edu92,age92){c |}{col 17}{res}{space 2}-.1857682{col 29}{space 2} .0401335{col 40}{space 1}   -4.63{col 49}{space 3}0.000{col 57}{space 4}-.2644284{col 70}{space 3} -.107108
{txt}{space 6}cov(edu92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2} .4723451{col 29}{space 2} .0335297{col 40}{space 1}   14.09{col 49}{space 3}0.000{col 57}{space 4}  .406628{col 70}{space 3} .5380621
{txt}{space 6}cov(edu92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2}  .019903{col 29}{space 2} .0468958{col 40}{space 1}    0.42{col 49}{space 3}0.671{col 57}{space 4} -.072011{col 70}{space 3}  .111817
{txt}{space 6}cov(edu92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.1562903{col 29}{space 2} .0404266{col 40}{space 1}   -3.87{col 49}{space 3}0.000{col 57}{space 4}-.2355249{col 70}{space 3}-.0770557
{txt}{space 6}cov(edu92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .1118528{col 29}{space 2} .0406471{col 40}{space 1}    2.75{col 49}{space 3}0.006{col 57}{space 4}  .032186{col 70}{space 3} .1915197
{txt}{space 6}cov(edu92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.1508577{col 29}{space 2} .0402489{col 40}{space 1}   -3.75{col 49}{space 3}0.000{col 57}{space 4}-.2297441{col 70}{space 3}-.0719712
{txt}{space 6}cov(edu92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0021448{col 29}{space 2} .0414374{col 40}{space 1}   -0.05{col 49}{space 3}0.959{col 57}{space 4}-.0833605{col 70}{space 3}  .079071
{txt}{space 6}cov(age92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2}-.0816022{col 29}{space 2} .0426691{col 40}{space 1}   -1.91{col 49}{space 3}0.056{col 57}{space 4}-.1652321{col 70}{space 3} .0020276
{txt}{space 6}cov(age92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2}-.0841066{col 29}{space 2} .0465957{col 40}{space 1}   -1.81{col 49}{space 3}0.071{col 57}{space 4}-.1754325{col 70}{space 3} .0072193
{txt}{space 6}cov(age92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}  .043197{col 29}{space 2} .0408509{col 40}{space 1}    1.06{col 49}{space 3}0.290{col 57}{space 4}-.0368693{col 70}{space 3} .1232633
{txt}{space 6}cov(age92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}  .089773{col 29}{space 2} .0405974{col 40}{space 1}    2.21{col 49}{space 3}0.027{col 57}{space 4} .0102035{col 70}{space 3} .1693425
{txt}{space 6}cov(age92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.0635972{col 29}{space 2} .0407617{col 40}{space 1}   -1.56{col 49}{space 3}0.119{col 57}{space 4}-.1434888{col 70}{space 3} .0162943
{txt}{space 6}cov(age92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0411586{col 29}{space 2} .0408579{col 40}{space 1}   -1.01{col 49}{space 3}0.314{col 57}{space 4}-.1212387{col 70}{space 3} .0389215
{txt}{space 3}cov(income92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} .1303785{col 29}{space 2} .0483633{col 40}{space 1}    2.70{col 49}{space 3}0.007{col 57}{space 4} .0355882{col 70}{space 3} .2251689
{txt}{space 3}cov(income92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.1467582{col 29}{space 2} .0411872{col 40}{space 1}   -3.56{col 49}{space 3}0.000{col 57}{space 4}-.2274836{col 70}{space 3}-.0660329
{txt}{space 3}cov(income92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .2110559{col 29}{space 2} .0405971{col 40}{space 1}    5.20{col 49}{space 3}0.000{col 57}{space 4}  .131487{col 70}{space 3} .2906248
{txt}{space 3}cov(income92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.2247494{col 29}{space 2} .0405544{col 40}{space 1}   -5.54{col 49}{space 3}0.000{col 57}{space 4}-.3042345{col 70}{space 3}-.1452643
{txt}{space 3}cov(income92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.1418565{col 29}{space 2} .0414676{col 40}{space 1}   -3.42{col 49}{space 3}0.001{col 57}{space 4}-.2231314{col 70}{space 3}-.0605815
{txt}{space 3}cov(church92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.1416673{col 29}{space 2} .0457733{col 40}{space 1}   -3.09{col 49}{space 3}0.002{col 57}{space 4}-.2313814{col 70}{space 3}-.0519532
{txt}{space 3}cov(church92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .1793968{col 29}{space 2} .0423296{col 40}{space 1}    4.24{col 49}{space 3}0.000{col 57}{space 4} .0964322{col 70}{space 3} .2623614
{txt}{space 3}cov(church92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.1694966{col 29}{space 2}   .04173{col 40}{space 1}   -4.06{col 49}{space 3}0.000{col 57}{space 4}-.2512859{col 70}{space 3}-.0877073
{txt}{space 3}cov(church92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.1298993{col 29}{space 2} .0452087{col 40}{space 1}   -2.87{col 49}{space 3}0.004{col 57}{space 4}-.2185068{col 70}{space 3}-.0412918
{txt}{space 3}cov(female92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}-.0664225{col 29}{space 2} .0407467{col 40}{space 1}   -1.63{col 49}{space 3}0.103{col 57}{space 4}-.1462846{col 70}{space 3} .0134396
{txt}{space 3}cov(female92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .0819539{col 29}{space 2} .0406524{col 40}{space 1}    2.02{col 49}{space 3}0.044{col 57}{space 4} .0022767{col 70}{space 3} .1616311
{txt}{space 3}cov(female92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} .0213853{col 29}{space 2} .0409086{col 40}{space 1}    0.52{col 49}{space 3}0.601{col 57}{space 4} -.058794{col 70}{space 3} .1015646
{txt}{space 4}cov(white92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.8801623{col 29}{space 2} .0092215{col 40}{space 1}  -95.45{col 49}{space 3}0.000{col 57}{space 4}-.8982361{col 70}{space 3}-.8620885
{txt}{space 4}cov(white92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.1940849{col 29}{space 2} .0393856{col 40}{space 1}   -4.93{col 49}{space 3}0.000{col 57}{space 4}-.2712793{col 70}{space 3}-.1168906
{txt}{space 4}cov(black92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} .1498615{col 29}{space 2} .0400081{col 40}{space 1}    3.75{col 49}{space 3}0.000{col 57}{space 4}  .071447{col 70}{space 3} .2282759
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
LR test of model vs. saturated: chi2({res:1})   = {res:    51.66}, Prob > chi2 = {res}0.0000
{txt}
{com}. estat gof, stats(all)   
{res}
{txt}{hline 21}{c TT}{hline 54}
{lalign 21:Fit statistic}{c |}      Value   Description
{hline 21}{c +}{hline 54}
{lalign 21:Likelihood ratio}{c |}
{ralign 20:chi2_ms({res:1})} {c |} {res}{ralign 10:    51.660}{txt}   model vs. saturated
{ralign 20:p > chi2} {c |} {res}{ralign 10:     0.000}
{txt}{ralign 20:chi2_bs({res:27})} {c |} {res}{ralign 10:   283.548}{txt}   baseline vs. saturated
{ralign 20:p > chi2} {c |} {res}{ralign 10:     0.000}
{txt}{hline 21}{c +}{hline 54}
{lalign 21:Population error}{c |}
{ralign 20:RMSEA} {c |} {res}{ralign 10:     0.291}{txt}   Root mean squared error of approximation
{ralign 20:90% CI, lower bound} {c |} {res}{ralign 10:     0.227}
{txt}{ralign 20:upper bound} {c |} {res}{ralign 10:     0.361}
{txt}{ralign 20:pclose} {c |} {res}{ralign 10:     0.000}{txt}   Probability RMSEA <= 0.05
{hline 21}{c +}{hline 54}
{lalign 21:Information criteria}{c |}
{ralign 20:AIC} {c |} {res}{ralign 10: 32885.768}{txt}   Akaike's information criterion
{ralign 20:BIC} {c |} {res}{ralign 10: 33474.285}{txt}   Bayesian information criterion
{hline 21}{c +}{hline 54}
{lalign 21:Baseline comparison}{c |}
{ralign 20:CFI} {c |} {res}{ralign 10:     0.803}{txt}   Comparative fit index
{ralign 20:TLI} {c |} {res}{ralign 10:    -4.332}{txt}   Tucker-Lewis index
{hline 21}{c +}{hline 54}
{lalign 21:Size of residuals}{c |}
{ralign 20:CD} {c |} {res}{ralign 10:     0.323}{txt}   Coefficient of determination
{hline 21}{c BT}{hline 54}
{p 0 2 2 75}Note: SRMR is not{txt} reported because of missing values.{p_end}

{com}. 
. * Model 2
. sem (valuepol96 <- valuepol92 diffideotherm92 sorting92 issextreme92 ///
>         interest92 edu92 age92 income92 church92 female92 white92 black92 south92) ///
>         (diffideotherm96 <- valuepol92 diffideotherm92 sorting92 issextreme92 ///
>         interest92 edu92 age92 income92 church92 female92 white92 black92 south92), ///
>         standardized method(mlmv)
{res}{txt}{p 0 6 2}note: Missing values found in observed exogenous variables. Using the {opt noxconditional} behavior. Specify the {opt forcexconditional} option to override this behavior.{p_end}
Endogenous variables

{p 0 11 2}Observed:{space 2}{res}valuepol96 diffideotherm96{p_end}
{txt}
Exogenous variables

{p 0 11 2}Observed:{space 2}{res}valuepol92 diffideotherm92 sorting92 issextreme92 interest92 edu92 age92 income92 church92 female92 white92 black92 south92{p_end}
{txt}
Fitting saturated model:

Iteration 0:{space 3}log likelihood = {res:-16034.134}  
Iteration 1:{space 3}log likelihood = {res:-15942.303}  
Iteration 2:{space 3}log likelihood = {res:-15840.038}  
Iteration 3:{space 3}log likelihood = {res:-15833.291}  
Iteration 4:{space 3}log likelihood = {res:-15833.073}  
Iteration 5:{space 3}log likelihood = {res:-15833.072}  

Fitting baseline model:

Iteration 0:{space 3}log likelihood = {res:-15991.926}  
Iteration 1:{space 3}log likelihood = {res:-15991.821}  
Iteration 2:{space 3}log likelihood = {res:-15991.821}  
{res}{txt}
Fitting target model:

Iteration 0:{space 3}log likelihood = {res:-15846.576}  
Iteration 1:{space 3}log likelihood = {res:-15845.744}  
Iteration 2:{space 3}log likelihood = {res:-15845.739}  
Iteration 3:{space 3}log likelihood = {res:-15845.739}  

{col 1}Structural equation model{col 49}Number of obs{col 67}= {res}       597
{txt}{col 1}Estimation method{col 20}= {res}mlmv
{txt}{col 1}Log likelihood{col 20}= {res}-15845.739

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}      OIM
{col 1}   Standardized{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Structural     {col 17}{txt}{c |}
{space 2}{col 3}valuepol96   {col 17}{c |}
{space 5}valuepol92 {c |}{col 17}{res}{space 2} .1967354{col 29}{space 2} .0468631{col 40}{space 1}    4.20{col 49}{space 3}0.000{col 57}{space 4} .1048853{col 70}{space 3} .2885855
{txt}{space 2}diffideoth~92 {c |}{col 17}{res}{space 2} .0775946{col 29}{space 2} .0482537{col 40}{space 1}    1.61{col 49}{space 3}0.108{col 57}{space 4} -.016981{col 70}{space 3} .1721701
{txt}{space 6}sorting92 {c |}{col 17}{res}{space 2} .1106983{col 29}{space 2} .0561191{col 40}{space 1}    1.97{col 49}{space 3}0.049{col 57}{space 4} .0007069{col 70}{space 3} .2206897
{txt}{space 3}issextreme92 {c |}{col 17}{res}{space 2} .0074244{col 29}{space 2} .0403939{col 40}{space 1}    0.18{col 49}{space 3}0.854{col 57}{space 4}-.0717461{col 70}{space 3} .0865949
{txt}{space 5}interest92 {c |}{col 17}{res}{space 2} .0120363{col 29}{space 2} .0414246{col 40}{space 1}    0.29{col 49}{space 3}0.771{col 57}{space 4}-.0691544{col 70}{space 3} .0932271
{txt}{space 10}edu92 {c |}{col 17}{res}{space 2} .0073664{col 29}{space 2} .0493246{col 40}{space 1}    0.15{col 49}{space 3}0.881{col 57}{space 4}-.0893081{col 70}{space 3} .1040409
{txt}{space 10}age92 {c |}{col 17}{res}{space 2}-.0698786{col 29}{space 2} .0401197{col 40}{space 1}   -1.74{col 49}{space 3}0.082{col 57}{space 4}-.1485117{col 70}{space 3} .0087546
{txt}{space 7}income92 {c |}{col 17}{res}{space 2} .0314583{col 29}{space 2} .0483693{col 40}{space 1}    0.65{col 49}{space 3}0.515{col 57}{space 4}-.0633438{col 70}{space 3} .1262603
{txt}{space 7}church92 {c |}{col 17}{res}{space 2} .0599762{col 29}{space 2} .0457798{col 40}{space 1}    1.31{col 49}{space 3}0.190{col 57}{space 4}-.0297505{col 70}{space 3}  .149703
{txt}{space 7}female92 {c |}{col 17}{res}{space 2} .0451887{col 29}{space 2} .0401973{col 40}{space 1}    1.12{col 49}{space 3}0.261{col 57}{space 4}-.0335965{col 70}{space 3}  .123974
{txt}{space 8}white92 {c |}{col 17}{res}{space 2}-.0164883{col 29}{space 2} .0834312{col 40}{space 1}   -0.20{col 49}{space 3}0.843{col 57}{space 4}-.1800104{col 70}{space 3} .1470338
{txt}{space 8}black92 {c |}{col 17}{res}{space 2} .0432614{col 29}{space 2} .0826928{col 40}{space 1}    0.52{col 49}{space 3}0.601{col 57}{space 4}-.1188136{col 70}{space 3} .2053364
{txt}{space 8}south92 {c |}{col 17}{res}{space 2}-.0187968{col 29}{space 2} .0401552{col 40}{space 1}   -0.47{col 49}{space 3}0.640{col 57}{space 4}-.0974994{col 70}{space 3} .0599059
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3601668{col 29}{space 2} .3476173{col 40}{space 1}    1.04{col 49}{space 3}0.300{col 57}{space 4}-.3211505{col 70}{space 3} 1.041484
{space 2}{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}diffideoth~96{col 17}{c |}
{space 5}valuepol92 {c |}{col 17}{res}{space 2} .0857931{col 29}{space 2}    .0427{col 40}{space 1}    2.01{col 49}{space 3}0.045{col 57}{space 4} .0021027{col 70}{space 3} .1694836
{txt}{space 2}diffideoth~92 {c |}{col 17}{res}{space 2} .4721331{col 29}{space 2} .0400793{col 40}{space 1}   11.78{col 49}{space 3}0.000{col 57}{space 4} .3935791{col 70}{space 3} .5506871
{txt}{space 6}sorting92 {c |}{col 17}{res}{space 2} .0691976{col 29}{space 2} .0484355{col 40}{space 1}    1.43{col 49}{space 3}0.153{col 57}{space 4}-.0257343{col 70}{space 3} .1641295
{txt}{space 3}issextreme92 {c |}{col 17}{res}{space 2} .0165584{col 29}{space 2} .0375742{col 40}{space 1}    0.44{col 49}{space 3}0.659{col 57}{space 4}-.0570857{col 70}{space 3} .0902026
{txt}{space 5}interest92 {c |}{col 17}{res}{space 2}-.0075158{col 29}{space 2} .0387342{col 40}{space 1}   -0.19{col 49}{space 3}0.846{col 57}{space 4}-.0834335{col 70}{space 3}  .068402
{txt}{space 10}edu92 {c |}{col 17}{res}{space 2} .1028194{col 29}{space 2} .0437039{col 40}{space 1}    2.35{col 49}{space 3}0.019{col 57}{space 4} .0171613{col 70}{space 3} .1884775
{txt}{space 10}age92 {c |}{col 17}{res}{space 2} .0578375{col 29}{space 2} .0385601{col 40}{space 1}    1.50{col 49}{space 3}0.134{col 57}{space 4} -.017739{col 70}{space 3} .1334139
{txt}{space 7}income92 {c |}{col 17}{res}{space 2} .0348299{col 29}{space 2} .0429596{col 40}{space 1}    0.81{col 49}{space 3}0.418{col 57}{space 4}-.0493694{col 70}{space 3} .1190291
{txt}{space 7}church92 {c |}{col 17}{res}{space 2}-.0113627{col 29}{space 2} .0422258{col 40}{space 1}   -0.27{col 49}{space 3}0.788{col 57}{space 4}-.0941238{col 70}{space 3} .0713983
{txt}{space 7}female92 {c |}{col 17}{res}{space 2} .0142875{col 29}{space 2} .0370894{col 40}{space 1}    0.39{col 49}{space 3}0.700{col 57}{space 4}-.0584064{col 70}{space 3} .0869813
{txt}{space 8}white92 {c |}{col 17}{res}{space 2} -.147933{col 29}{space 2} .0762091{col 40}{space 1}   -1.94{col 49}{space 3}0.052{col 57}{space 4}-.2973002{col 70}{space 3} .0014342
{txt}{space 8}black92 {c |}{col 17}{res}{space 2}-.1901872{col 29}{space 2} .0765984{col 40}{space 1}   -2.48{col 49}{space 3}0.013{col 57}{space 4}-.3403173{col 70}{space 3}-.0400571
{txt}{space 8}south92 {c |}{col 17}{res}{space 2}-.0320086{col 29}{space 2}  .036916{col 40}{space 1}   -0.87{col 49}{space 3}0.386{col 57}{space 4}-.1043625{col 70}{space 3} .0403454
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3677905{col 29}{space 2} .3247388{col 40}{space 1}    1.13{col 49}{space 3}0.257{col 57}{space 4}-.2686858{col 70}{space 3} 1.004267
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mean(valuepol92){c |}{col 17}{res}{space 2} .8532282{col 29}{space 2} .0477991{col 40}{space 1}   17.85{col 49}{space 3}0.000{col 57}{space 4} .7595436{col 70}{space 3} .9469127
{txt}mean(diffide~92){c |}{col 17}{res}{space 2} .9622299{col 29}{space 2} .0514981{col 40}{space 1}   18.68{col 49}{space 3}0.000{col 57}{space 4} .8612954{col 70}{space 3} 1.063164
{txt}{space 1}mean(sorting92){c |}{col 17}{res}{space 2} .8642052{col 29}{space 2} .0545496{col 40}{space 1}   15.84{col 49}{space 3}0.000{col 57}{space 4} .7572899{col 70}{space 3} .9711205
{txt}mean(issextr~92){c |}{col 17}{res}{space 2} 2.015185{col 29}{space 2} .0715994{col 40}{space 1}   28.15{col 49}{space 3}0.000{col 57}{space 4} 1.874853{col 70}{space 3} 2.155517
{txt}mean(interest92){c |}{col 17}{res}{space 2} 3.640924{col 29}{space 2} .1132081{col 40}{space 1}   32.16{col 49}{space 3}0.000{col 57}{space 4}  3.41904{col 70}{space 3} 3.862807
{txt}{space 5}mean(edu92){c |}{col 17}{res}{space 2} 2.445431{col 29}{space 2} .0828775{col 40}{space 1}   29.51{col 49}{space 3}0.000{col 57}{space 4} 2.282994{col 70}{space 3} 2.607868
{txt}{space 5}mean(age92){c |}{col 17}{res}{space 2} 2.599566{col 29}{space 2} .0856434{col 40}{space 1}   30.35{col 49}{space 3}0.000{col 57}{space 4} 2.431708{col 70}{space 3} 2.767424
{txt}{space 2}mean(income92){c |}{col 17}{res}{space 2} 2.295763{col 29}{space 2}  .082266{col 40}{space 1}   27.91{col 49}{space 3}0.000{col 57}{space 4} 2.134525{col 70}{space 3} 2.457002
{txt}{space 2}mean(church92){c |}{col 17}{res}{space 2} 3.205193{col 29}{space 2} .1131507{col 40}{space 1}   28.33{col 49}{space 3}0.000{col 57}{space 4} 2.983422{col 70}{space 3} 3.426965
{txt}{space 2}mean(female92){c |}{col 17}{res}{space 2} 1.046297{col 29}{space 2} .0509108{col 40}{space 1}   20.55{col 49}{space 3}0.000{col 57}{space 4} .9465134{col 70}{space 3}  1.14608
{txt}{space 3}mean(white92){c |}{col 17}{res}{space 2} 2.270383{col 29}{space 2}  .077409{col 40}{space 1}   29.33{col 49}{space 3}0.000{col 57}{space 4} 2.118664{col 70}{space 3} 2.422102
{txt}{space 3}mean(black92){c |}{col 17}{res}{space 2} .3876713{col 29}{space 2} .0424372{col 40}{space 1}    9.14{col 49}{space 3}0.000{col 57}{space 4}  .304496{col 70}{space 3} .4708466
{txt}{space 3}mean(south92){c |}{col 17}{res}{space 2}  .758418{col 29}{space 2} .0464412{col 40}{space 1}   16.33{col 49}{space 3}0.000{col 57}{space 4}  .667395{col 70}{space 3}  .849441
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
var(e.value~l96){c |}{col 17}{res}{space 2} .8875784{col 29}{space 2} .0248108{col 57}{space 4} .8402583{col 70}{space 3} .9375633
{txt}var(e.diffid~96){c |}{col 17}{res}{space 2} .6541521{col 29}{space 2} .0333804{col 57}{space 4} .5918931{col 70}{space 3} .7229599
{txt}{space 1}var(valuepol92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}var(diffideo~92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 2}var(sorting92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}var(issextre~92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 1}var(interest92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 6}var(edu92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 6}var(age92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 3}var(income92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 3}var(church92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 3}var(female92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 4}var(white92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 4}var(black92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 4}var(south92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}cov(valuepol92,{c |}
diffideotherm92){c |}{col 17}{res}{space 2}  .235607{col 29}{space 2} .0391492{col 40}{space 1}    6.02{col 49}{space 3}0.000{col 57}{space 4} .1588761{col 70}{space 3}  .312338
{txt}{space 1}cov(valuepol92,{c |}
{space 6}sorting92){c |}{col 17}{res}{space 2} .5086101{col 29}{space 2}  .032259{col 40}{space 1}   15.77{col 49}{space 3}0.000{col 57}{space 4} .4453836{col 70}{space 3} .5718367
{txt}{space 1}cov(valuepol92,{c |}
{space 3}issextreme92){c |}{col 17}{res}{space 2} .0843187{col 29}{space 2} .0407659{col 40}{space 1}    2.07{col 49}{space 3}0.039{col 57}{space 4}  .004419{col 70}{space 3} .1642184
{txt}{space 1}cov(valuepol92,{c |}
{space 5}interest92){c |}{col 17}{res}{space 2} .1473218{col 29}{space 2} .0400477{col 40}{space 1}    3.68{col 49}{space 3}0.000{col 57}{space 4} .0688298{col 70}{space 3} .2258138
{txt}{space 1}cov(valuepol92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2} .1714036{col 29}{space 2} .0399979{col 40}{space 1}    4.29{col 49}{space 3}0.000{col 57}{space 4} .0930091{col 70}{space 3} .2497981
{txt}{space 1}cov(valuepol92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2}-.0040948{col 29}{space 2} .0409266{col 40}{space 1}   -0.10{col 49}{space 3}0.920{col 57}{space 4}-.0843094{col 70}{space 3} .0761199
{txt}{space 1}cov(valuepol92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2}  .083841{col 29}{space 2} .0416067{col 40}{space 1}    2.02{col 49}{space 3}0.044{col 57}{space 4} .0022933{col 70}{space 3} .1653887
{txt}{space 1}cov(valuepol92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2}-.0311762{col 29}{space 2} .0459716{col 40}{space 1}   -0.68{col 49}{space 3}0.498{col 57}{space 4}-.1212788{col 70}{space 3} .0589265
{txt}{space 1}cov(valuepol92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2} .0849649{col 29}{space 2} .0406318{col 40}{space 1}    2.09{col 49}{space 3}0.037{col 57}{space 4}  .005328{col 70}{space 3} .1646018
{txt}{space 1}cov(valuepol92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}-.0488647{col 29}{space 2} .0408296{col 40}{space 1}   -1.20{col 49}{space 3}0.231{col 57}{space 4}-.1288892{col 70}{space 3} .0311597
{txt}{space 1}cov(valuepol92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .0787568{col 29}{space 2} .0406734{col 40}{space 1}    1.94{col 49}{space 3}0.053{col 57}{space 4}-.0009616{col 70}{space 3} .1584753
{txt}{space 1}cov(valuepol92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} -.028927{col 29}{space 2}  .040893{col 40}{space 1}   -0.71{col 49}{space 3}0.479{col 57}{space 4}-.1090759{col 70}{space 3} .0512218
{txt}cov(diffideo~92,{c |}
{space 6}sorting92){c |}{col 17}{res}{space 2} .4396096{col 29}{space 2} .0360604{col 40}{space 1}   12.19{col 49}{space 3}0.000{col 57}{space 4} .3689325{col 70}{space 3} .5102867
{txt}cov(diffideo~92,{c |}
{space 3}issextreme92){c |}{col 17}{res}{space 2} .0921224{col 29}{space 2} .0421835{col 40}{space 1}    2.18{col 49}{space 3}0.029{col 57}{space 4} .0094443{col 70}{space 3} .1748005
{txt}cov(diffideo~92,{c |}
{space 5}interest92){c |}{col 17}{res}{space 2} .1600058{col 29}{space 2} .0420871{col 40}{space 1}    3.80{col 49}{space 3}0.000{col 57}{space 4} .0775167{col 70}{space 3} .2424949
{txt}cov(diffideo~92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2} .1751253{col 29}{space 2} .0419886{col 40}{space 1}    4.17{col 49}{space 3}0.000{col 57}{space 4} .0928292{col 70}{space 3} .2574214
{txt}cov(diffideo~92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2}-.0050805{col 29}{space 2} .0430848{col 40}{space 1}   -0.12{col 49}{space 3}0.906{col 57}{space 4}-.0895251{col 70}{space 3} .0793641
{txt}cov(diffideo~92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2} .1485142{col 29}{space 2}   .04272{col 40}{space 1}    3.48{col 49}{space 3}0.001{col 57}{space 4} .0647846{col 70}{space 3} .2322438
{txt}cov(diffideo~92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2}-.1128483{col 29}{space 2} .0474429{col 40}{space 1}   -2.38{col 49}{space 3}0.017{col 57}{space 4}-.2058346{col 70}{space 3} -.019862
{txt}cov(diffideo~92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.0646233{col 29}{space 2} .0417099{col 40}{space 1}   -1.55{col 49}{space 3}0.121{col 57}{space 4}-.1463732{col 70}{space 3} .0171267
{txt}cov(diffideo~92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .2018762{col 29}{space 2} .0408138{col 40}{space 1}    4.95{col 49}{space 3}0.000{col 57}{space 4} .1218826{col 70}{space 3} .2818698
{txt}cov(diffideo~92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.1724456{col 29}{space 2} .0413285{col 40}{space 1}   -4.17{col 49}{space 3}0.000{col 57}{space 4}-.2534479{col 70}{space 3}-.0914432
{txt}cov(diffideo~92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} .0087478{col 29}{space 2} .0421853{col 40}{space 1}    0.21{col 49}{space 3}0.836{col 57}{space 4}-.0739339{col 70}{space 3} .0914294
{txt}{space 2}cov(sorting92,{c |}
{space 3}issextreme92){c |}{col 17}{res}{space 2}  .051449{col 29}{space 2} .0466802{col 40}{space 1}    1.10{col 49}{space 3}0.270{col 57}{space 4}-.0400426{col 70}{space 3} .1429406
{txt}{space 2}cov(sorting92,{c |}
{space 5}interest92){c |}{col 17}{res}{space 2} .1771797{col 29}{space 2} .0458223{col 40}{space 1}    3.87{col 49}{space 3}0.000{col 57}{space 4} .0873696{col 70}{space 3} .2669898
{txt}{space 2}cov(sorting92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2} .1377139{col 29}{space 2} .0450742{col 40}{space 1}    3.06{col 49}{space 3}0.002{col 57}{space 4}   .04937{col 70}{space 3} .2260577
{txt}{space 2}cov(sorting92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2} .0275152{col 29}{space 2} .0444131{col 40}{space 1}    0.62{col 49}{space 3}0.536{col 57}{space 4}-.0595327{col 70}{space 3} .1145632
{txt}{space 2}cov(sorting92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2} .0691905{col 29}{space 2} .0463011{col 40}{space 1}    1.49{col 49}{space 3}0.135{col 57}{space 4} -.021558{col 70}{space 3}  .159939
{txt}{space 2}cov(sorting92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2}-.0415199{col 29}{space 2} .0488375{col 40}{space 1}   -0.85{col 49}{space 3}0.395{col 57}{space 4}-.1372397{col 70}{space 3} .0541999
{txt}{space 2}cov(sorting92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.0268849{col 29}{space 2} .0442717{col 40}{space 1}   -0.61{col 49}{space 3}0.544{col 57}{space 4}-.1136559{col 70}{space 3} .0598861
{txt}{space 2}cov(sorting92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .0092911{col 29}{space 2} .0478177{col 40}{space 1}    0.19{col 49}{space 3}0.846{col 57}{space 4}-.0844298{col 70}{space 3}  .103012
{txt}{space 2}cov(sorting92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .0163252{col 29}{space 2} .0485345{col 40}{space 1}    0.34{col 49}{space 3}0.737{col 57}{space 4}-.0788008{col 70}{space 3} .1114511
{txt}{space 2}cov(sorting92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0313315{col 29}{space 2} .0454768{col 40}{space 1}   -0.69{col 49}{space 3}0.491{col 57}{space 4}-.1204643{col 70}{space 3} .0578014
{txt}cov(issextre~92,{c |}
{space 5}interest92){c |}{col 17}{res}{space 2}-.0414736{col 29}{space 2}   .04179{col 40}{space 1}   -0.99{col 49}{space 3}0.321{col 57}{space 4}-.1233806{col 70}{space 3} .0404333
{txt}cov(issextre~92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2}-.0885389{col 29}{space 2}  .041484{col 40}{space 1}   -2.13{col 49}{space 3}0.033{col 57}{space 4}-.1698459{col 70}{space 3}-.0072318
{txt}cov(issextre~92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2} .0107705{col 29}{space 2} .0412269{col 40}{space 1}    0.26{col 49}{space 3}0.794{col 57}{space 4}-.0700327{col 70}{space 3} .0915738
{txt}cov(issextre~92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2}-.1117242{col 29}{space 2} .0423065{col 40}{space 1}   -2.64{col 49}{space 3}0.008{col 57}{space 4}-.1946434{col 70}{space 3}-.0288051
{txt}cov(issextre~92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} .0282857{col 29}{space 2} .0451935{col 40}{space 1}    0.63{col 49}{space 3}0.531{col 57}{space 4}-.0602919{col 70}{space 3} .1168632
{txt}cov(issextre~92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2} .0519825{col 29}{space 2} .0410383{col 40}{space 1}    1.27{col 49}{space 3}0.205{col 57}{space 4}-.0284512{col 70}{space 3} .1324161
{txt}cov(issextre~92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}-.1073678{col 29}{space 2} .0408274{col 40}{space 1}   -2.63{col 49}{space 3}0.009{col 57}{space 4} -.187388{col 70}{space 3}-.0273476
{txt}cov(issextre~92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .1262781{col 29}{space 2} .0407391{col 40}{space 1}    3.10{col 49}{space 3}0.002{col 57}{space 4} .0464309{col 70}{space 3} .2061253
{txt}cov(issextre~92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} .0347896{col 29}{space 2} .0412114{col 40}{space 1}    0.84{col 49}{space 3}0.399{col 57}{space 4}-.0459832{col 70}{space 3} .1155624
{txt}{space 1}cov(interest92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2} .2906311{col 29}{space 2} .0381453{col 40}{space 1}    7.62{col 49}{space 3}0.000{col 57}{space 4} .2158676{col 70}{space 3} .3653946
{txt}{space 1}cov(interest92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2} .0272965{col 29}{space 2} .0409193{col 40}{space 1}    0.67{col 49}{space 3}0.505{col 57}{space 4}-.0529038{col 70}{space 3} .1074967
{txt}{space 1}cov(interest92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2} .2064736{col 29}{space 2} .0412724{col 40}{space 1}    5.00{col 49}{space 3}0.000{col 57}{space 4} .1255811{col 70}{space 3} .2873661
{txt}{space 1}cov(interest92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} .0139202{col 29}{space 2} .0468019{col 40}{space 1}    0.30{col 49}{space 3}0.766{col 57}{space 4}-.0778098{col 70}{space 3} .1056501
{txt}{space 1}cov(interest92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.0672397{col 29}{space 2} .0408056{col 40}{space 1}   -1.65{col 49}{space 3}0.099{col 57}{space 4}-.1472171{col 70}{space 3} .0127377
{txt}{space 1}cov(interest92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .0304332{col 29}{space 2} .0409015{col 40}{space 1}    0.74{col 49}{space 3}0.457{col 57}{space 4}-.0497322{col 70}{space 3} .1105986
{txt}{space 1}cov(interest92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.0368434{col 29}{space 2} .0408812{col 40}{space 1}   -0.90{col 49}{space 3}0.367{col 57}{space 4} -.116969{col 70}{space 3} .0432822
{txt}{space 1}cov(interest92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0024034{col 29}{space 2} .0409987{col 40}{space 1}   -0.06{col 49}{space 3}0.953{col 57}{space 4}-.0827593{col 70}{space 3} .0779526
{txt}cov(edu92,age92){c |}{col 17}{res}{space 2}-.1865389{col 29}{space 2} .0401128{col 40}{space 1}   -4.65{col 49}{space 3}0.000{col 57}{space 4}-.2651585{col 70}{space 3}-.1079192
{txt}{space 6}cov(edu92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2} .4738101{col 29}{space 2} .0334321{col 40}{space 1}   14.17{col 49}{space 3}0.000{col 57}{space 4} .4082844{col 70}{space 3} .5393358
{txt}{space 6}cov(edu92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} .0225202{col 29}{space 2} .0466902{col 40}{space 1}    0.48{col 49}{space 3}0.630{col 57}{space 4} -.068991{col 70}{space 3} .1140313
{txt}{space 6}cov(edu92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.1549767{col 29}{space 2} .0404459{col 40}{space 1}   -3.83{col 49}{space 3}0.000{col 57}{space 4}-.2342491{col 70}{space 3}-.0757042
{txt}{space 6}cov(edu92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .1109649{col 29}{space 2} .0406583{col 40}{space 1}    2.73{col 49}{space 3}0.006{col 57}{space 4} .0312761{col 70}{space 3} .1906537
{txt}{space 6}cov(edu92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.1498848{col 29}{space 2} .0402662{col 40}{space 1}   -3.72{col 49}{space 3}0.000{col 57}{space 4}-.2288052{col 70}{space 3}-.0709645
{txt}{space 6}cov(edu92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0001949{col 29}{space 2} .0414268{col 40}{space 1}   -0.00{col 49}{space 3}0.996{col 57}{space 4}-.0813899{col 70}{space 3} .0810001
{txt}{space 6}cov(age92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2}-.0818709{col 29}{space 2} .0426582{col 40}{space 1}   -1.92{col 49}{space 3}0.055{col 57}{space 4}-.1654794{col 70}{space 3} .0017377
{txt}{space 6}cov(age92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} -.087096{col 29}{space 2} .0463632{col 40}{space 1}   -1.88{col 49}{space 3}0.060{col 57}{space 4}-.1779663{col 70}{space 3} .0037742
{txt}{space 6}cov(age92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}  .043197{col 29}{space 2} .0408509{col 40}{space 1}    1.06{col 49}{space 3}0.290{col 57}{space 4}-.0368693{col 70}{space 3} .1232633
{txt}{space 6}cov(age92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}  .089773{col 29}{space 2} .0405974{col 40}{space 1}    2.21{col 49}{space 3}0.027{col 57}{space 4} .0102035{col 70}{space 3} .1693425
{txt}{space 6}cov(age92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.0635972{col 29}{space 2} .0407617{col 40}{space 1}   -1.56{col 49}{space 3}0.119{col 57}{space 4}-.1434888{col 70}{space 3} .0162943
{txt}{space 6}cov(age92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0411586{col 29}{space 2} .0408579{col 40}{space 1}   -1.01{col 49}{space 3}0.314{col 57}{space 4}-.1212387{col 70}{space 3} .0389215
{txt}{space 3}cov(income92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} .1318998{col 29}{space 2} .0480981{col 40}{space 1}    2.74{col 49}{space 3}0.006{col 57}{space 4} .0376294{col 70}{space 3} .2261703
{txt}{space 3}cov(income92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.1456923{col 29}{space 2} .0412087{col 40}{space 1}   -3.54{col 49}{space 3}0.000{col 57}{space 4}-.2264599{col 70}{space 3}-.0649248
{txt}{space 3}cov(income92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .2083613{col 29}{space 2} .0406712{col 40}{space 1}    5.12{col 49}{space 3}0.000{col 57}{space 4} .1286472{col 70}{space 3} .2880753
{txt}{space 3}cov(income92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.2227896{col 29}{space 2} .0406213{col 40}{space 1}   -5.48{col 49}{space 3}0.000{col 57}{space 4}-.3024059{col 70}{space 3}-.1431733
{txt}{space 3}cov(income92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.1407652{col 29}{space 2} .0414833{col 40}{space 1}   -3.39{col 49}{space 3}0.001{col 57}{space 4} -.222071{col 70}{space 3}-.0594593
{txt}{space 3}cov(church92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.1477955{col 29}{space 2} .0455061{col 40}{space 1}   -3.25{col 49}{space 3}0.001{col 57}{space 4}-.2369859{col 70}{space 3}-.0586051
{txt}{space 3}cov(church92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .1827801{col 29}{space 2} .0421657{col 40}{space 1}    4.33{col 49}{space 3}0.000{col 57}{space 4} .1001369{col 70}{space 3} .2654234
{txt}{space 3}cov(church92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.1722553{col 29}{space 2} .0415985{col 40}{space 1}   -4.14{col 49}{space 3}0.000{col 57}{space 4}-.2537868{col 70}{space 3}-.0907237
{txt}{space 3}cov(church92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.1337366{col 29}{space 2} .0449647{col 40}{space 1}   -2.97{col 49}{space 3}0.003{col 57}{space 4}-.2218658{col 70}{space 3}-.0456074
{txt}{space 3}cov(female92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}-.0664225{col 29}{space 2} .0407467{col 40}{space 1}   -1.63{col 49}{space 3}0.103{col 57}{space 4}-.1462846{col 70}{space 3} .0134396
{txt}{space 3}cov(female92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .0819539{col 29}{space 2} .0406524{col 40}{space 1}    2.02{col 49}{space 3}0.044{col 57}{space 4} .0022767{col 70}{space 3} .1616311
{txt}{space 3}cov(female92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} .0213853{col 29}{space 2} .0409086{col 40}{space 1}    0.52{col 49}{space 3}0.601{col 57}{space 4} -.058794{col 70}{space 3} .1015646
{txt}{space 4}cov(white92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.8801623{col 29}{space 2} .0092215{col 40}{space 1}  -95.45{col 49}{space 3}0.000{col 57}{space 4}-.8982361{col 70}{space 3}-.8620885
{txt}{space 4}cov(white92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.1940849{col 29}{space 2} .0393856{col 40}{space 1}   -4.93{col 49}{space 3}0.000{col 57}{space 4}-.2712793{col 70}{space 3}-.1168906
{txt}{space 4}cov(black92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} .1498615{col 29}{space 2} .0400081{col 40}{space 1}    3.75{col 49}{space 3}0.000{col 57}{space 4}  .071447{col 70}{space 3} .2282759
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
LR test of model vs. saturated: chi2({res:1})   = {res:    25.33}, Prob > chi2 = {res}0.0000
{txt}
{com}. estat gof, stats(all)   
{res}
{txt}{hline 21}{c TT}{hline 54}
{lalign 21:Fit statistic}{c |}      Value   Description
{hline 21}{c +}{hline 54}
{lalign 21:Likelihood ratio}{c |}
{ralign 20:chi2_ms({res:1})} {c |} {res}{ralign 10:    25.333}{txt}   model vs. saturated
{ralign 20:p > chi2} {c |} {res}{ralign 10:     0.000}
{txt}{ralign 20:chi2_bs({res:27})} {c |} {res}{ralign 10:   317.497}{txt}   baseline vs. saturated
{ralign 20:p > chi2} {c |} {res}{ralign 10:     0.000}
{txt}{hline 21}{c +}{hline 54}
{lalign 21:Population error}{c |}
{ralign 20:RMSEA} {c |} {res}{ralign 10:     0.202}{txt}   Root mean squared error of approximation
{ralign 20:90% CI, lower bound} {c |} {res}{ralign 10:     0.139}
{txt}{ralign 20:upper bound} {c |} {res}{ralign 10:     0.273}
{txt}{ralign 20:pclose} {c |} {res}{ralign 10:     0.000}{txt}   Probability RMSEA <= 0.05
{hline 21}{c +}{hline 54}
{lalign 21:Information criteria}{c |}
{ralign 20:AIC} {c |} {res}{ralign 10: 31959.478}{txt}   Akaike's information criterion
{ralign 20:BIC} {c |} {res}{ralign 10: 32547.995}{txt}   Bayesian information criterion
{hline 21}{c +}{hline 54}
{lalign 21:Baseline comparison}{c |}
{ralign 20:CFI} {c |} {res}{ralign 10:     0.916}{txt}   Comparative fit index
{ralign 20:TLI} {c |} {res}{ralign 10:    -1.262}{txt}   Tucker-Lewis index
{hline 21}{c +}{hline 54}
{lalign 21:Size of residuals}{c |}
{ralign 20:CD} {c |} {res}{ralign 10:     0.411}{txt}   Coefficient of determination
{hline 21}{c BT}{hline 54}
{p 0 2 2 75}Note: SRMR is not{txt} reported because of missing values.{p_end}

{com}. 
. * Model 3
. sem (valuepol96 <- valuepol92 diffcandtherm92 sorting92 issextreme92 ///
>         interest92 edu92 age92 income92 church92 female92 white92 black92 south92) ///
>         (diffcandtherm96 <- valuepol92 diffcandtherm92 sorting92 issextreme92 ///
>         interest92 edu92 age92 income92 church92 female92 white92 black92 south92), ///
>         standardized method(mlmv) 
{res}{txt}{p 0 6 2}note: Missing values found in observed exogenous variables. Using the {opt noxconditional} behavior. Specify the {opt forcexconditional} option to override this behavior.{p_end}
Endogenous variables

{p 0 11 2}Observed:{space 2}{res}valuepol96 diffcandtherm96{p_end}
{txt}
Exogenous variables

{p 0 11 2}Observed:{space 2}{res}valuepol92 diffcandtherm92 sorting92 issextreme92 interest92 edu92 age92 income92 church92 female92 white92 black92 south92{p_end}
{txt}
Fitting saturated model:

Iteration 0:{space 3}log likelihood = {res:-16496.115}  
Iteration 1:{space 3}log likelihood = {res:-16412.281}  
Iteration 2:{space 3}log likelihood = {res:-16383.655}  
Iteration 3:{space 3}log likelihood = {res: -16383.34}  
Iteration 4:{space 3}log likelihood = {res: -16383.34}  

Fitting baseline model:

Iteration 0:{space 3}log likelihood = {res:-16480.629}  
Iteration 1:{space 3}log likelihood = {res:-16480.567}  
Iteration 2:{space 3}log likelihood = {res:-16480.567}  
{res}{txt}
Fitting target model:

Iteration 0:{space 3}log likelihood = {res:-16392.645}  
Iteration 1:{space 3}log likelihood = {res:-16392.609}  
Iteration 2:{space 3}log likelihood = {res:-16392.609}  

{col 1}Structural equation model{col 49}Number of obs{col 67}= {res}       597
{txt}{col 1}Estimation method{col 20}= {res}mlmv
{txt}{col 1}Log likelihood{col 20}= {res}-16392.609

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}      OIM
{col 1}   Standardized{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Structural     {col 17}{txt}{c |}
{space 2}{col 3}valuepol96   {col 17}{c |}
{space 5}valuepol92 {c |}{col 17}{res}{space 2} .1928631{col 29}{space 2} .0472838{col 40}{space 1}    4.08{col 49}{space 3}0.000{col 57}{space 4} .1001887{col 70}{space 3} .2855376
{txt}{space 2}diffcandth~92 {c |}{col 17}{res}{space 2} .0295941{col 29}{space 2} .0428973{col 40}{space 1}    0.69{col 49}{space 3}0.490{col 57}{space 4}-.0544831{col 70}{space 3} .1136712
{txt}{space 6}sorting92 {c |}{col 17}{res}{space 2} .1404286{col 29}{space 2} .0524995{col 40}{space 1}    2.67{col 49}{space 3}0.007{col 57}{space 4} .0375315{col 70}{space 3} .2433257
{txt}{space 3}issextreme92 {c |}{col 17}{res}{space 2} .0124162{col 29}{space 2} .0403716{col 40}{space 1}    0.31{col 49}{space 3}0.758{col 57}{space 4}-.0667107{col 70}{space 3} .0915431
{txt}{space 5}interest92 {c |}{col 17}{res}{space 2} .0116265{col 29}{space 2} .0420565{col 40}{space 1}    0.28{col 49}{space 3}0.782{col 57}{space 4}-.0708027{col 70}{space 3} .0940557
{txt}{space 10}edu92 {c |}{col 17}{res}{space 2} .0079001{col 29}{space 2} .0494682{col 40}{space 1}    0.16{col 49}{space 3}0.873{col 57}{space 4}-.0890558{col 70}{space 3}  .104856
{txt}{space 10}age92 {c |}{col 17}{res}{space 2}-.0750348{col 29}{space 2} .0401494{col 40}{space 1}   -1.87{col 49}{space 3}0.062{col 57}{space 4}-.1537263{col 70}{space 3} .0036566
{txt}{space 7}income92 {c |}{col 17}{res}{space 2}  .038985{col 29}{space 2} .0484188{col 40}{space 1}    0.81{col 49}{space 3}0.421{col 57}{space 4} -.055914{col 70}{space 3} .1338841
{txt}{space 7}church92 {c |}{col 17}{res}{space 2} .0418671{col 29}{space 2} .0448777{col 40}{space 1}    0.93{col 49}{space 3}0.351{col 57}{space 4}-.0460917{col 70}{space 3} .1298258
{txt}{space 7}female92 {c |}{col 17}{res}{space 2} .0389028{col 29}{space 2} .0402932{col 40}{space 1}    0.97{col 49}{space 3}0.334{col 57}{space 4}-.0400704{col 70}{space 3} .1178761
{txt}{space 8}white92 {c |}{col 17}{res}{space 2} -.003159{col 29}{space 2}  .083323{col 40}{space 1}   -0.04{col 49}{space 3}0.970{col 57}{space 4}-.1664691{col 70}{space 3} .1601511
{txt}{space 8}black92 {c |}{col 17}{res}{space 2} .0389514{col 29}{space 2} .0830064{col 40}{space 1}    0.47{col 49}{space 3}0.639{col 57}{space 4}-.1237382{col 70}{space 3}  .201641
{txt}{space 8}south92 {c |}{col 17}{res}{space 2}-.0150402{col 29}{space 2} .0401921{col 40}{space 1}   -0.37{col 49}{space 3}0.708{col 57}{space 4}-.0938153{col 70}{space 3} .0637348
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3883619{col 29}{space 2} .3480288{col 40}{space 1}    1.12{col 49}{space 3}0.264{col 57}{space 4} -.293762{col 70}{space 3} 1.070486
{space 2}{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}diffcandth~96{col 17}{c |}
{space 5}valuepol92 {c |}{col 17}{res}{space 2} .0500962{col 29}{space 2} .0464675{col 40}{space 1}    1.08{col 49}{space 3}0.281{col 57}{space 4}-.0409784{col 70}{space 3} .1411709
{txt}{space 2}diffcandth~92 {c |}{col 17}{res}{space 2} .2894662{col 29}{space 2}  .040193{col 40}{space 1}    7.20{col 49}{space 3}0.000{col 57}{space 4} .2106893{col 70}{space 3} .3682431
{txt}{space 6}sorting92 {c |}{col 17}{res}{space 2} .1234584{col 29}{space 2} .0517313{col 40}{space 1}    2.39{col 49}{space 3}0.017{col 57}{space 4} .0220668{col 70}{space 3} .2248499
{txt}{space 3}issextreme92 {c |}{col 17}{res}{space 2} .0904244{col 29}{space 2} .0388602{col 40}{space 1}    2.33{col 49}{space 3}0.020{col 57}{space 4} .0142598{col 70}{space 3} .1665891
{txt}{space 5}interest92 {c |}{col 17}{res}{space 2} .0750186{col 29}{space 2} .0409514{col 40}{space 1}    1.83{col 49}{space 3}0.067{col 57}{space 4}-.0052446{col 70}{space 3} .1552818
{txt}{space 10}edu92 {c |}{col 17}{res}{space 2}-.0621356{col 29}{space 2} .0474857{col 40}{space 1}   -1.31{col 49}{space 3}0.191{col 57}{space 4}-.1552058{col 70}{space 3} .0309346
{txt}{space 10}age92 {c |}{col 17}{res}{space 2}-.0220958{col 29}{space 2} .0393759{col 40}{space 1}   -0.56{col 49}{space 3}0.575{col 57}{space 4}-.0992711{col 70}{space 3} .0550795
{txt}{space 7}income92 {c |}{col 17}{res}{space 2} .0503601{col 29}{space 2} .0469249{col 40}{space 1}    1.07{col 49}{space 3}0.283{col 57}{space 4} -.041611{col 70}{space 3} .1423313
{txt}{space 7}church92 {c |}{col 17}{res}{space 2} -.020574{col 29}{space 2} .0439221{col 40}{space 1}   -0.47{col 49}{space 3}0.639{col 57}{space 4}-.1066598{col 70}{space 3} .0655117
{txt}{space 7}female92 {c |}{col 17}{res}{space 2} .0143358{col 29}{space 2} .0391861{col 40}{space 1}    0.37{col 49}{space 3}0.714{col 57}{space 4}-.0624675{col 70}{space 3} .0911391
{txt}{space 8}white92 {c |}{col 17}{res}{space 2} .0371902{col 29}{space 2} .0801557{col 40}{space 1}    0.46{col 49}{space 3}0.643{col 57}{space 4} -.119912{col 70}{space 3} .1942924
{txt}{space 8}black92 {c |}{col 17}{res}{space 2} .0630839{col 29}{space 2} .0802846{col 40}{space 1}    0.79{col 49}{space 3}0.432{col 57}{space 4}-.0942711{col 70}{space 3} .2204389
{txt}{space 8}south92 {c |}{col 17}{res}{space 2}-.0636218{col 29}{space 2} .0391449{col 40}{space 1}   -1.63{col 49}{space 3}0.104{col 57}{space 4}-.1403444{col 70}{space 3} .0131007
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .5613764{col 29}{space 2} .3381069{col 40}{space 1}    1.66{col 49}{space 3}0.097{col 57}{space 4}-.1013009{col 70}{space 3} 1.224054
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
mean(valuepol92){c |}{col 17}{res}{space 2} .8532282{col 29}{space 2} .0477991{col 40}{space 1}   17.85{col 49}{space 3}0.000{col 57}{space 4} .7595436{col 70}{space 3} .9469127
{txt}mean(diffcan~92){c |}{col 17}{res}{space 2} 1.402036{col 29}{space 2} .0588426{col 40}{space 1}   23.83{col 49}{space 3}0.000{col 57}{space 4} 1.286707{col 70}{space 3} 1.517365
{txt}{space 1}mean(sorting92){c |}{col 17}{res}{space 2} .8800255{col 29}{space 2} .0548319{col 40}{space 1}   16.05{col 49}{space 3}0.000{col 57}{space 4}  .772557{col 70}{space 3} .9874941
{txt}mean(issextr~92){c |}{col 17}{res}{space 2} 2.014958{col 29}{space 2} .0716048{col 40}{space 1}   28.14{col 49}{space 3}0.000{col 57}{space 4} 1.874615{col 70}{space 3}   2.1553
{txt}mean(interest92){c |}{col 17}{res}{space 2} 3.641453{col 29}{space 2}  .113196{col 40}{space 1}   32.17{col 49}{space 3}0.000{col 57}{space 4} 3.419593{col 70}{space 3} 3.863313
{txt}{space 5}mean(edu92){c |}{col 17}{res}{space 2} 2.446275{col 29}{space 2} .0828808{col 40}{space 1}   29.52{col 49}{space 3}0.000{col 57}{space 4} 2.283831{col 70}{space 3} 2.608718
{txt}{space 5}mean(age92){c |}{col 17}{res}{space 2} 2.599566{col 29}{space 2} .0856434{col 40}{space 1}   30.35{col 49}{space 3}0.000{col 57}{space 4} 2.431708{col 70}{space 3} 2.767424
{txt}{space 2}mean(income92){c |}{col 17}{res}{space 2} 2.295077{col 29}{space 2} .0823076{col 40}{space 1}   27.88{col 49}{space 3}0.000{col 57}{space 4} 2.133757{col 70}{space 3} 2.456397
{txt}{space 2}mean(church92){c |}{col 17}{res}{space 2} 3.200756{col 29}{space 2} .1133371{col 40}{space 1}   28.24{col 49}{space 3}0.000{col 57}{space 4}  2.97862{col 70}{space 3} 3.422893
{txt}{space 2}mean(female92){c |}{col 17}{res}{space 2} 1.046297{col 29}{space 2} .0509108{col 40}{space 1}   20.55{col 49}{space 3}0.000{col 57}{space 4} .9465134{col 70}{space 3}  1.14608
{txt}{space 3}mean(white92){c |}{col 17}{res}{space 2} 2.270383{col 29}{space 2}  .077409{col 40}{space 1}   29.33{col 49}{space 3}0.000{col 57}{space 4} 2.118664{col 70}{space 3} 2.422102
{txt}{space 3}mean(black92){c |}{col 17}{res}{space 2} .3876713{col 29}{space 2} .0424372{col 40}{space 1}    9.14{col 49}{space 3}0.000{col 57}{space 4}  .304496{col 70}{space 3} .4708466
{txt}{space 3}mean(south92){c |}{col 17}{res}{space 2}  .758418{col 29}{space 2} .0464412{col 40}{space 1}   16.33{col 49}{space 3}0.000{col 57}{space 4}  .667395{col 70}{space 3}  .849441
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
var(e.value~l96){c |}{col 17}{res}{space 2} .8907838{col 29}{space 2} .0246889{col 57}{space 4} .8436853{col 70}{space 3} .9405115
{txt}var(e.diffca~96){c |}{col 17}{res}{space 2} .8236543{col 29}{space 2} .0291577{col 57}{space 4} .7684437{col 70}{space 3} .8828315
{txt}{space 1}var(valuepol92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}var(diffcand~92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 2}var(sorting92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}var(issextre~92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 1}var(interest92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 6}var(edu92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 6}var(age92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 3}var(income92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 3}var(church92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 3}var(female92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 4}var(white92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 4}var(black92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{space 4}var(south92){c |}{col 17}{res}{space 2}        1{col 29}{space 2}        .{col 57}{space 4}        .{col 70}{space 3}        .
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}cov(valuepol92,{c |}
diffcandtherm92){c |}{col 17}{res}{space 2} .2297595{col 29}{space 2} .0390749{col 40}{space 1}    5.88{col 49}{space 3}0.000{col 57}{space 4} .1531741{col 70}{space 3} .3063449
{txt}{space 1}cov(valuepol92,{c |}
{space 6}sorting92){c |}{col 17}{res}{space 2}  .510511{col 29}{space 2}  .032285{col 40}{space 1}   15.81{col 49}{space 3}0.000{col 57}{space 4} .4472335{col 70}{space 3} .5737885
{txt}{space 1}cov(valuepol92,{c |}
{space 3}issextreme92){c |}{col 17}{res}{space 2} .0845323{col 29}{space 2} .0407634{col 40}{space 1}    2.07{col 49}{space 3}0.038{col 57}{space 4} .0046375{col 70}{space 3} .1644272
{txt}{space 1}cov(valuepol92,{c |}
{space 5}interest92){c |}{col 17}{res}{space 2} .1473582{col 29}{space 2} .0400468{col 40}{space 1}    3.68{col 49}{space 3}0.000{col 57}{space 4}  .068868{col 70}{space 3} .2258485
{txt}{space 1}cov(valuepol92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2} .1718231{col 29}{space 2} .0399913{col 40}{space 1}    4.30{col 49}{space 3}0.000{col 57}{space 4} .0934417{col 70}{space 3} .2502046
{txt}{space 1}cov(valuepol92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2}-.0040948{col 29}{space 2} .0409266{col 40}{space 1}   -0.10{col 49}{space 3}0.920{col 57}{space 4}-.0843094{col 70}{space 3} .0761199
{txt}{space 1}cov(valuepol92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2}  .083378{col 29}{space 2} .0416149{col 40}{space 1}    2.00{col 49}{space 3}0.045{col 57}{space 4} .0018143{col 70}{space 3} .1649416
{txt}{space 1}cov(valuepol92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2}-.0325393{col 29}{space 2} .0461531{col 40}{space 1}   -0.71{col 49}{space 3}0.481{col 57}{space 4}-.1229977{col 70}{space 3} .0579191
{txt}{space 1}cov(valuepol92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2} .0849649{col 29}{space 2} .0406318{col 40}{space 1}    2.09{col 49}{space 3}0.037{col 57}{space 4}  .005328{col 70}{space 3} .1646018
{txt}{space 1}cov(valuepol92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}-.0488647{col 29}{space 2} .0408296{col 40}{space 1}   -1.20{col 49}{space 3}0.231{col 57}{space 4}-.1288892{col 70}{space 3} .0311597
{txt}{space 1}cov(valuepol92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .0787568{col 29}{space 2} .0406734{col 40}{space 1}    1.94{col 49}{space 3}0.053{col 57}{space 4}-.0009616{col 70}{space 3} .1584753
{txt}{space 1}cov(valuepol92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} -.028927{col 29}{space 2}  .040893{col 40}{space 1}   -0.71{col 49}{space 3}0.479{col 57}{space 4}-.1090759{col 70}{space 3} .0512218
{txt}cov(diffcand~92,{c |}
{space 6}sorting92){c |}{col 17}{res}{space 2} .2760737{col 29}{space 2} .0417701{col 40}{space 1}    6.61{col 49}{space 3}0.000{col 57}{space 4} .1942059{col 70}{space 3} .3579416
{txt}cov(diffcand~92,{c |}
{space 3}issextreme92){c |}{col 17}{res}{space 2} .1115107{col 29}{space 2} .0417285{col 40}{space 1}    2.67{col 49}{space 3}0.008{col 57}{space 4} .0297244{col 70}{space 3}  .193297
{txt}cov(diffcand~92,{c |}
{space 5}interest92){c |}{col 17}{res}{space 2} .2016581{col 29}{space 2} .0411657{col 40}{space 1}    4.90{col 49}{space 3}0.000{col 57}{space 4} .1209749{col 70}{space 3} .2823413
{txt}cov(diffcand~92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2} .0802252{col 29}{space 2} .0422985{col 40}{space 1}    1.90{col 49}{space 3}0.058{col 57}{space 4}-.0026783{col 70}{space 3} .1631287
{txt}cov(diffcand~92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2} .0426091{col 29}{space 2} .0417385{col 40}{space 1}    1.02{col 49}{space 3}0.307{col 57}{space 4}-.0391968{col 70}{space 3} .1244151
{txt}cov(diffcand~92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2} .0007062{col 29}{space 2} .0435879{col 40}{space 1}    0.02{col 49}{space 3}0.987{col 57}{space 4}-.0847246{col 70}{space 3} .0861369
{txt}cov(diffcand~92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} -.021686{col 29}{space 2} .0454539{col 40}{space 1}   -0.48{col 49}{space 3}0.633{col 57}{space 4}-.1107739{col 70}{space 3} .0674019
{txt}cov(diffcand~92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2} .0594781{col 29}{space 2} .0413567{col 40}{space 1}    1.44{col 49}{space 3}0.150{col 57}{space 4}-.0215794{col 70}{space 3} .1405357
{txt}cov(diffcand~92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .0808239{col 29}{space 2} .0418359{col 40}{space 1}    1.93{col 49}{space 3}0.053{col 57}{space 4} -.001173{col 70}{space 3} .1628208
{txt}cov(diffcand~92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.0358844{col 29}{space 2} .0423497{col 40}{space 1}   -0.85{col 49}{space 3}0.397{col 57}{space 4}-.1188883{col 70}{space 3} .0471196
{txt}cov(diffcand~92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0487124{col 29}{space 2} .0415248{col 40}{space 1}   -1.17{col 49}{space 3}0.241{col 57}{space 4}-.1300995{col 70}{space 3} .0326747
{txt}{space 2}cov(sorting92,{c |}
{space 3}issextreme92){c |}{col 17}{res}{space 2} .0673865{col 29}{space 2} .0470589{col 40}{space 1}    1.43{col 49}{space 3}0.152{col 57}{space 4}-.0248472{col 70}{space 3} .1596202
{txt}{space 2}cov(sorting92,{c |}
{space 5}interest92){c |}{col 17}{res}{space 2} .1746346{col 29}{space 2} .0463757{col 40}{space 1}    3.77{col 49}{space 3}0.000{col 57}{space 4} .0837399{col 70}{space 3} .2655294
{txt}{space 2}cov(sorting92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2} .1245368{col 29}{space 2} .0456487{col 40}{space 1}    2.73{col 49}{space 3}0.006{col 57}{space 4} .0350671{col 70}{space 3} .2140066
{txt}{space 2}cov(sorting92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2} .0322921{col 29}{space 2}  .044665{col 40}{space 1}    0.72{col 49}{space 3}0.470{col 57}{space 4}-.0552498{col 70}{space 3} .1198339
{txt}{space 2}cov(sorting92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2}  .062054{col 29}{space 2} .0466856{col 40}{space 1}    1.33{col 49}{space 3}0.184{col 57}{space 4}-.0294481{col 70}{space 3} .1535562
{txt}{space 2}cov(sorting92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} -.034196{col 29}{space 2} .0493864{col 40}{space 1}   -0.69{col 49}{space 3}0.489{col 57}{space 4}-.1309916{col 70}{space 3} .0625995
{txt}{space 2}cov(sorting92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.0270637{col 29}{space 2} .0445805{col 40}{space 1}   -0.61{col 49}{space 3}0.544{col 57}{space 4}-.1144399{col 70}{space 3} .0603124
{txt}{space 2}cov(sorting92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .0001051{col 29}{space 2} .0484237{col 40}{space 1}    0.00{col 49}{space 3}0.998{col 57}{space 4}-.0948037{col 70}{space 3} .0950138
{txt}{space 2}cov(sorting92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .0246321{col 29}{space 2} .0491904{col 40}{space 1}    0.50{col 49}{space 3}0.617{col 57}{space 4}-.0717794{col 70}{space 3} .1210436
{txt}{space 2}cov(sorting92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0265474{col 29}{space 2} .0459031{col 40}{space 1}   -0.58{col 49}{space 3}0.563{col 57}{space 4}-.1165158{col 70}{space 3} .0634209
{txt}cov(issextre~92,{c |}
{space 5}interest92){c |}{col 17}{res}{space 2}-.0388231{col 29}{space 2} .0417925{col 40}{space 1}   -0.93{col 49}{space 3}0.353{col 57}{space 4} -.120735{col 70}{space 3} .0430887
{txt}cov(issextre~92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2}-.0898852{col 29}{space 2} .0414794{col 40}{space 1}   -2.17{col 49}{space 3}0.030{col 57}{space 4}-.1711833{col 70}{space 3} -.008587
{txt}cov(issextre~92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2} .0110556{col 29}{space 2} .0412264{col 40}{space 1}    0.27{col 49}{space 3}0.789{col 57}{space 4}-.0697467{col 70}{space 3} .0918579
{txt}cov(issextre~92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2}-.1113158{col 29}{space 2} .0423071{col 40}{space 1}   -2.63{col 49}{space 3}0.009{col 57}{space 4}-.1942363{col 70}{space 3}-.0283954
{txt}cov(issextre~92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} .0307775{col 29}{space 2} .0453066{col 40}{space 1}    0.68{col 49}{space 3}0.497{col 57}{space 4}-.0580219{col 70}{space 3} .1195768
{txt}cov(issextre~92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2} .0522754{col 29}{space 2} .0410355{col 40}{space 1}    1.27{col 49}{space 3}0.203{col 57}{space 4}-.0281527{col 70}{space 3} .1327035
{txt}cov(issextre~92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}-.1077978{col 29}{space 2}  .040821{col 40}{space 1}   -2.64{col 49}{space 3}0.008{col 57}{space 4}-.1878055{col 70}{space 3}-.0277902
{txt}cov(issextre~92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .1267352{col 29}{space 2} .0407309{col 40}{space 1}    3.11{col 49}{space 3}0.002{col 57}{space 4} .0469041{col 70}{space 3} .2065663
{txt}cov(issextre~92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}  .034584{col 29}{space 2} .0412124{col 40}{space 1}    0.84{col 49}{space 3}0.401{col 57}{space 4}-.0461907{col 70}{space 3} .1153588
{txt}{space 1}cov(interest92,{c |}
{space 10}edu92){c |}{col 17}{res}{space 2} .2920934{col 29}{space 2} .0381064{col 40}{space 1}    7.67{col 49}{space 3}0.000{col 57}{space 4} .2174062{col 70}{space 3} .3667806
{txt}{space 1}cov(interest92,{c |}
{space 10}age92){c |}{col 17}{res}{space 2} .0275971{col 29}{space 2} .0409177{col 40}{space 1}    0.67{col 49}{space 3}0.500{col 57}{space 4}   -.0526{col 70}{space 3} .1077943
{txt}{space 1}cov(interest92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2} .2055432{col 29}{space 2} .0412966{col 40}{space 1}    4.98{col 49}{space 3}0.000{col 57}{space 4} .1246033{col 70}{space 3}  .286483
{txt}{space 1}cov(interest92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} .0176442{col 29}{space 2} .0469821{col 40}{space 1}    0.38{col 49}{space 3}0.707{col 57}{space 4} -.074439{col 70}{space 3} .1097274
{txt}{space 1}cov(interest92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.0678367{col 29}{space 2} .0407991{col 40}{space 1}   -1.66{col 49}{space 3}0.096{col 57}{space 4}-.1478015{col 70}{space 3} .0121281
{txt}{space 1}cov(interest92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .0305591{col 29}{space 2} .0409006{col 40}{space 1}    0.75{col 49}{space 3}0.455{col 57}{space 4}-.0496047{col 70}{space 3} .1107229
{txt}{space 1}cov(interest92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.0369549{col 29}{space 2} .0408804{col 40}{space 1}   -0.90{col 49}{space 3}0.366{col 57}{space 4}-.1170789{col 70}{space 3} .0431692
{txt}{space 1}cov(interest92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0017235{col 29}{space 2} .0409969{col 40}{space 1}   -0.04{col 49}{space 3}0.966{col 57}{space 4} -.082076{col 70}{space 3}  .078629
{txt}cov(edu92,age92){c |}{col 17}{res}{space 2}-.1854457{col 29}{space 2} .0401397{col 40}{space 1}   -4.62{col 49}{space 3}0.000{col 57}{space 4}-.2641182{col 70}{space 3}-.1067733
{txt}{space 6}cov(edu92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2} .4723998{col 29}{space 2} .0335406{col 40}{space 1}   14.08{col 49}{space 3}0.000{col 57}{space 4} .4066613{col 70}{space 3} .5381382
{txt}{space 6}cov(edu92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} .0218051{col 29}{space 2} .0469031{col 40}{space 1}    0.46{col 49}{space 3}0.642{col 57}{space 4}-.0701232{col 70}{space 3} .1137335
{txt}{space 6}cov(edu92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.1567252{col 29}{space 2} .0404182{col 40}{space 1}   -3.88{col 49}{space 3}0.000{col 57}{space 4}-.2359434{col 70}{space 3}-.0775071
{txt}{space 6}cov(edu92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .1120585{col 29}{space 2} .0406448{col 40}{space 1}    2.76{col 49}{space 3}0.006{col 57}{space 4} .0323961{col 70}{space 3} .1917209
{txt}{space 6}cov(edu92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.1510524{col 29}{space 2} .0402459{col 40}{space 1}   -3.75{col 49}{space 3}0.000{col 57}{space 4} -.229933{col 70}{space 3}-.0721718
{txt}{space 6}cov(edu92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0026086{col 29}{space 2} .0414347{col 40}{space 1}   -0.06{col 49}{space 3}0.950{col 57}{space 4}-.0838191{col 70}{space 3} .0786019
{txt}{space 6}cov(age92,{c |}
{space 7}income92){c |}{col 17}{res}{space 2}-.0817343{col 29}{space 2} .0426794{col 40}{space 1}   -1.92{col 49}{space 3}0.055{col 57}{space 4}-.1653844{col 70}{space 3} .0019158
{txt}{space 6}cov(age92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2}-.0864562{col 29}{space 2} .0465908{col 40}{space 1}   -1.86{col 49}{space 3}0.064{col 57}{space 4}-.1777725{col 70}{space 3}   .00486
{txt}{space 6}cov(age92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}  .043197{col 29}{space 2} .0408509{col 40}{space 1}    1.06{col 49}{space 3}0.290{col 57}{space 4}-.0368693{col 70}{space 3} .1232633
{txt}{space 6}cov(age92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}  .089773{col 29}{space 2} .0405974{col 40}{space 1}    2.21{col 49}{space 3}0.027{col 57}{space 4} .0102035{col 70}{space 3} .1693425
{txt}{space 6}cov(age92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.0635972{col 29}{space 2} .0407617{col 40}{space 1}   -1.56{col 49}{space 3}0.119{col 57}{space 4}-.1434888{col 70}{space 3} .0162943
{txt}{space 6}cov(age92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.0411586{col 29}{space 2} .0408579{col 40}{space 1}   -1.01{col 49}{space 3}0.314{col 57}{space 4}-.1212387{col 70}{space 3} .0389215
{txt}{space 3}cov(income92,{c |}
{space 7}church92){c |}{col 17}{res}{space 2} .1279995{col 29}{space 2}  .048396{col 40}{space 1}    2.64{col 49}{space 3}0.008{col 57}{space 4}  .033145{col 70}{space 3}  .222854
{txt}{space 3}cov(income92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.1456694{col 29}{space 2} .0412121{col 40}{space 1}   -3.53{col 49}{space 3}0.000{col 57}{space 4}-.2264437{col 70}{space 3}-.0648951
{txt}{space 3}cov(income92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}  .210616{col 29}{space 2} .0406175{col 40}{space 1}    5.19{col 49}{space 3}0.000{col 57}{space 4} .1310071{col 70}{space 3} .2902248
{txt}{space 3}cov(income92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.2246425{col 29}{space 2} .0405707{col 40}{space 1}   -5.54{col 49}{space 3}0.000{col 57}{space 4}-.3041596{col 70}{space 3}-.1451255
{txt}{space 3}cov(income92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.1414005{col 29}{space 2} .0414782{col 40}{space 1}   -3.41{col 49}{space 3}0.001{col 57}{space 4}-.2226963{col 70}{space 3}-.0601047
{txt}{space 3}cov(church92,{c |}
{space 7}female92){c |}{col 17}{res}{space 2}-.1411628{col 29}{space 2} .0457725{col 40}{space 1}   -3.08{col 49}{space 3}0.002{col 57}{space 4}-.2308753{col 70}{space 3}-.0514503
{txt}{space 3}cov(church92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2} .1800823{col 29}{space 2} .0423101{col 40}{space 1}    4.26{col 49}{space 3}0.000{col 57}{space 4} .0971561{col 70}{space 3} .2630086
{txt}{space 3}cov(church92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.1705947{col 29}{space 2} .0416936{col 40}{space 1}   -4.09{col 49}{space 3}0.000{col 57}{space 4}-.2523127{col 70}{space 3}-.0888767
{txt}{space 3}cov(church92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.1297193{col 29}{space 2} .0452396{col 40}{space 1}   -2.87{col 49}{space 3}0.004{col 57}{space 4}-.2183873{col 70}{space 3}-.0410514
{txt}{space 3}cov(female92,{c |}
{space 8}white92){c |}{col 17}{res}{space 2}-.0664225{col 29}{space 2} .0407467{col 40}{space 1}   -1.63{col 49}{space 3}0.103{col 57}{space 4}-.1462846{col 70}{space 3} .0134396
{txt}{space 3}cov(female92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2} .0819539{col 29}{space 2} .0406524{col 40}{space 1}    2.02{col 49}{space 3}0.044{col 57}{space 4} .0022767{col 70}{space 3} .1616311
{txt}{space 3}cov(female92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} .0213853{col 29}{space 2} .0409086{col 40}{space 1}    0.52{col 49}{space 3}0.601{col 57}{space 4} -.058794{col 70}{space 3} .1015646
{txt}{space 4}cov(white92,{c |}
{space 8}black92){c |}{col 17}{res}{space 2}-.8801623{col 29}{space 2} .0092215{col 40}{space 1}  -95.45{col 49}{space 3}0.000{col 57}{space 4}-.8982361{col 70}{space 3}-.8620885
{txt}{space 4}cov(white92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2}-.1940849{col 29}{space 2} .0393856{col 40}{space 1}   -4.93{col 49}{space 3}0.000{col 57}{space 4}-.2712793{col 70}{space 3}-.1168906
{txt}{space 4}cov(black92,{c |}
{space 8}south92){c |}{col 17}{res}{space 2} .1498615{col 29}{space 2} .0400081{col 40}{space 1}    3.75{col 49}{space 3}0.000{col 57}{space 4}  .071447{col 70}{space 3} .2282759
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
LR test of model vs. saturated: chi2({res:1})   = {res:    18.54}, Prob > chi2 = {res}0.0000
{txt}
{com}. estat gof, stats(all)
{res}
{txt}{hline 21}{c TT}{hline 54}
{lalign 21:Fit statistic}{c |}      Value   Description
{hline 21}{c +}{hline 54}
{lalign 21:Likelihood ratio}{c |}
{ralign 20:chi2_ms({res:1})} {c |} {res}{ralign 10:    18.538}{txt}   model vs. saturated
{ralign 20:p > chi2} {c |} {res}{ralign 10:     0.000}
{txt}{ralign 20:chi2_bs({res:27})} {c |} {res}{ralign 10:   194.455}{txt}   baseline vs. saturated
{ralign 20:p > chi2} {c |} {res}{ralign 10:     0.000}
{txt}{hline 21}{c +}{hline 54}
{lalign 21:Population error}{c |}
{ralign 20:RMSEA} {c |} {res}{ralign 10:     0.171}{txt}   Root mean squared error of approximation
{ralign 20:90% CI, lower bound} {c |} {res}{ralign 10:     0.109}
{txt}{ralign 20:upper bound} {c |} {res}{ralign 10:     0.244}
{txt}{ralign 20:pclose} {c |} {res}{ralign 10:     0.001}{txt}   Probability RMSEA <= 0.05
{hline 21}{c +}{hline 54}
{lalign 21:Information criteria}{c |}
{ralign 20:AIC} {c |} {res}{ralign 10: 33053.218}{txt}   Akaike's information criterion
{ralign 20:BIC} {c |} {res}{ralign 10: 33641.735}{txt}   Bayesian information criterion
{hline 21}{c +}{hline 54}
{lalign 21:Baseline comparison}{c |}
{ralign 20:CFI} {c |} {res}{ralign 10:     0.895}{txt}   Comparative fit index
{ralign 20:TLI} {c |} {res}{ralign 10:    -1.828}{txt}   Tucker-Lewis index
{hline 21}{c +}{hline 54}
{lalign 21:Size of residuals}{c |}
{ralign 20:CD} {c |} {res}{ralign 10:     0.260}{txt}   Coefficient of determination
{hline 21}{c BT}{hline 54}
{p 0 2 2 75}Note: SRMR is not{txt} reported because of missing values.{p_end}

{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/adamenders/Dropbox/Value Polarization and Affective Polarization/Code and Data/For Dataverse/Stata log file.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}20 Mar 2020, 16:40:40
{txt}{.-}
{smcl}
{txt}{sf}{ul off}